diff --git a/README.md b/README.md index d63a6a1..4cdd99a 100644 --- a/README.md +++ b/README.md @@ -1,11 +1,225 @@ **University of Pennsylvania, CIS 565: GPU Programming and Architecture, Project 1 - Flocking** -* (TODO) YOUR NAME HERE - * (TODO) [LinkedIn](), [personal website](), [twitter](), etc. -* Tested on: (TODO) Windows 22, i7-2222 @ 2.22GHz 22GB, GTX 222 222MB (Moore 2222 Lab) +* Xuntong Liang + * [LinkedIn](https://www.linkedin.com/in/xuntong-liang-406429181/), [GitHub](https://github.com/PacosLelouch), [twitter](https://twitter.com/XTL90234545). +* Tested on: Windows 10, i7-10750H @ 2.60GHz 16GB, RTX 2070 Super with Max-Q 8192MB -### (TODO: Your README) -Include screenshots, analysis, etc. (Remember, this is public, so don't put -anything here that you don't want to share with the world.) + +![Main result](images/readme/MainResult.png) + + + +## Play Around + +First of all, let's play with 5K boids. + +### Play Around with 5K Boids + +I opened V-sync to capture the result with 5K boids. Otherwise the boids would move in crazy speed due to the extremely high frame rate. + +![Play around with 5K boids (1)](images/readme/Boid5K_1.gif) + + + +After a while, some boids went together and became clusters. + +![Play around with 5K boids (2)](images/readme/Boid5K_2.gif) + + + +### Play Around with 60K Boids + +The result with 60K boids also consists of the process of aggregating clusters. + +![Play around with 60K boids (1)](images/readme/Boid60K_1.gif) + + + +It seems like pouring some paints :sweat_drops:. + +![Play around with 60K boids (2)](images/readme/Boid60K_2.gif) + + + +### Play Around with 1M Boids + +I think the result is not so pretty because it is too crowdy! However, it turns out to run at a good frame rate even with visualization. + +![Play around with 1M boids](images/readme/Boid1M_1.gif) + + + +## Tasks + +I manage to implement all the required method to simulate the boids flocking: + +1. Naive: Naively traverse all the other boids. +2. Scattered: Create a uniform grid for searching. +3. Coherent: Based on the uniform grid, align the scattered velocity and position attributes in memory coherently to improve the memory access. + +Also, I optimize the grid loop for adaptable neighbor searching in different grid cell width. + +Shared memory is not implemented. + + + +## Questions about Performance Analysis + +First of all, there are some questions and answers about the performance: + +* Q1: For each implementation, how does changing the number of boids affect + performance? Why do you think this is? + +* A1: Commonly, increasing the number of boids would increase the duration of each frame, thus decrease the frame rate, because this makes the average number of the neighbors of each boid increase, which increase the duration of updating velocity. For the `Scattered` and `Coherent` method, the time of sorting is also affected. + + + +* Q2: For each implementation, how does changing the block count and block size + affect performance? Why do you think this is? + +* A2: When the number of boids is small (for example, 5000), it does not affect too much because the main cost is sorting, which is independent of the block size. When the number of boids increase, the main cost gradually becomes updating velocity. When updating velocity, there exists a latency issue of accessing the memory. In this situation, containing more wraps in each block may be able to hide the latency, thus to increase the performance. + + + +* Q3: For the coherent uniform grid: did you experience any performance improvements + with the more coherent uniform grid? Was this the outcome you expected? + Why or why not? + +* A3: The more boids to simulate, the more improvements I experience. In my opinion, maintaining coherent uniform grids needs extra reshuffle operations, but access the memory in a better way. When the number of boids is small, the performance improvements do not appear because of the trade-off. However, when the number of boids become larger, the improvements become significant, since the cost of reshuffle operations increases a little but the strategy of memory access becomes much more important. + + + +* Q4: Did changing cell width and checking 27 vs 8 neighboring cells affect performance? + Why or why not? Be careful: it is insufficient (and possibly incorrect) to say + that 27-cell is slower simply because there are more cells to check! + +* A4: It does affect performance. Actually, checking 27 neighboring cells with 5.0 in width performs better than checking 8 neighboring cells with 10.0 in width. I think the performance improvements come from the fewer neighboring boids to consider, so it appears better when the number of boids is large. However, when the number of boids is small, it seems not so crowdy that the number of neighborhoods is not affected too much. Moreover, smaller grid cell width causes more grid cells, and it turns out to increase the cost of resetting `gridCellStartIndices` and `gridCellEndIndices`. It may cause another trade-off. + + + +See [further analysis](#performance-analysis). + + + +## Performance Analysis + +I recorded the data of each run, starting at the frame 512 and recording the 1024 consecutive frames. Then average the data per frame. + +First of all, analyze the performance with increasing number of boids for naive, scattered uniform grid, and coherent uniform grid, with and without visualization. These are run with the default settings of the block size and the neighboring search. + + + +### Performance with Increasing Number of Boids + +First check the framerate. Because the framerate of Naive is too low when the number of boids is large, I did not record the performance since I suppose the result is useless. + +![Framerate with increasing number of objects](images/readme/FPSBoid.png) + +![Framerate with increasing number of objects with visualization](images/readme/FPSBoidVis1.png) + +![Framerate with increasing number of objects with and without visualization](images/readme/FPSBoidVis0Vis1.png) + + + +It turns out that the performance is affected much with the number of boids. Also, the performance increases rapidly with uniform grid, and the performance is even better with coherent boids data in most cases. As the answer of [questions](#questions-about-performance-analysis) describes, I cannot tell the performance is better with coherent boids data when the number of boids is small. The duration of each event is needed. + + + +Then check the event duration: + +![Events duration for Naive comparing the number of objects](images/readme/EventDurationNaiveBoid.png) + +![Events duration for Scattered comparing the number of objects](images/readme/EventDurationScatteredBoid.png) + +![Events duration for Coherent comparing the number of objects](images/readme/EventDurationCoherentBoid.png) + + + +With increasing boid count, the cost of `kernUpdateVel` increases rapidly, and the cost of `thrust::sort_by_key` is also significant. + +The results are the same with visualization: + +![Events duration for Naive comparing the number of objects with visualization](images/readme/EventDurationNaiveBoidVis1.png) + +![Events duration for Scattered comparing the number of objects with visualization](images/readme/EventDurationScatteredBoidVis1.png) + +![Events duration for Coherent comparing the number of objects with visualization](images/readme/EventDurationCoherentBoidVis1.png) + + + +We can see that `kernUpdateVelNeighborSearchCoherent` is faster than `kernUpdateVelNeighborSearchScattered` in all cases, but it needs to reshuffle to maintain the coherent boids data, as the answer of [questions](#questions-about-performance-analysis) describes. As a result, `Coherent` affects little in cases with very few boids, but affects much in cases with more boids. + + + +### Performance with Different Block Size + +Changing the block size affects the underlying behaviors of the GPU. I analyzed with `Coherent` method and 8 neighboring cells, and cases with 5K boids, 100K boids, and 1M boids respectively. Framerates are shown below. + +![Framerate for Coherent with 5K comparing the block size](images/readme/FPSCoherentBoid5KBlockSize.png) + +![Framerate for Coherent with 100K comparing the block size](images/readme/FPSCoherentBoid100KBlockSize.png) + +![Framerate for Coherent with 1M comparing the block size](images/readme/FPSCoherentBoid1MBlockSize.png) + + + +I guess that the latency issue is bigger when the number of boids is larger, so the performance increases monotonically with 1M boids, but it does not happen with 100K boids and 5K boids. + +Further analysis on each event shows more information. + +![Event duration for Coherent with 5K comparing the block size](images/readme/EventDurationCoherentBoid5KBlockSize.png) + +![Event duration for Coherent with 100K comparing the block size](images/readme/EventDurationCoherentBoid100KBlockSize.png) + +![Event duration for Coherent with 1M comparing the block size](images/readme/EventDurationCoherentBoid1MBlockSize.png) + + + +As the answer of [questions](#questions-about-performance-analysis) describes, when the number of boids is small, the main cost comes from `thrust::sort_by_key`, which is not controlled by block size, so changing the block size rarely affects the performance. When the number of boids is large, the main cost comes from `kernUpdateVelNeighborSearchCoherent`, and it produces much latency of memory access. + + + +### Performance with Different Grid Cell Width + +When changing grid cell width, the neighboring cells may change as well. I analyzed with `Coherent` method and 128 threads per block, and cases with 5K boids, 100K boids, and 1M boids respectively. Framerates are shown below. + +![Framerate for Coherent with 5K comparing the grid cell width and neighbor count](images/readme/FPSCoherentBoid5KGCWidthNeiCnt.png) + +![Framerate for Coherent with 100K comparing the grid cell width and neighbor count](images/readme/FPSCoherentBoid100KGCWidthNeiCnt.png) + +![Framerate for Coherent with 1M comparing the grid cell width and neighbor count](images/readme/FPSCoherentBoid1MGCWidthNeiCnt.png) + + + +It turns out that in 5K boids case, the default setting performs better. In 100K and 1M cases, 5.0 width with 27 neighboring cells performs better. It can run over 100 hz without visualization even with 1M boids. + +I analyzed the duration of CUDA events and found that `kernUpdateVelNeighborSearchCoherent` and `kernResetIntBuffer` for each cell are affected much. Here are the statistic of them. + +![Duration of updating velocity for Coherent with 5K comparing the grid cell width and neighbor count](images/readme/UpdateVelCoherentBoid5KGCWidthNeiCnt.png) + +![Duration of updating velocity for Coherent with 100K comparing the grid cell width and neighbor count](images/readme/UpdateVelCoherentBoid100KGCWidthNeiCnt.png) + +![Duration of updating velocity for Coherent with 1M comparing the grid cell width and neighbor count](images/readme/UpdateVelCoherentBoid1MGCWidthNeiCnt.png) + + + +The duration of `kernUpdateVelNeighborSearchCoherent` shows that `5.0&27` is always the best setting, while in 5K boid case, the duration of `5.0&27` is nearly equal to the duration of `10.0&8`. + +So what about `kernResetIntBuffer`? + +![Duration of resetting start and end for Coherent with 5K comparing the grid cell width and neighbor count](images/readme/ResetStartEndCoherentBoid5KGCWidthNeiCnt.png) + +![Duration of resetting start and end for Coherent with 100K comparing the grid cell width and neighbor count](images/readme/ResetStartEndCoherentBoid100KGCWidthNeiCnt.png) + +![Duration of resetting start and end for Coherent with 1M comparing the grid cell width and neighbor count](images/readme/ResetStartEndCoherentBoid1MGCWidthNeiCnt.png) + + + +Generally, the duration of `kernResetIntBuffer` for each cell decreases when the grid cell width increases. I guess that even though this kernel should be execute parallel, the very large count of grid cell really matters because of the latency of starting a wrap, accessing global memory, etc. And because the cost of `kernResetIntBuffer` with `5.0&27` is higher than that with `10.0&8`, the performance of `5.0&27` is not better than the performance of `10.0&8` in 5K boids case is reasonable. + + + +- Raw data of performance analysis can be found in this repository. + diff --git a/images/readme/Boid1M_1.gif b/images/readme/Boid1M_1.gif new file mode 100644 index 0000000..a6c9c8d Binary files /dev/null and b/images/readme/Boid1M_1.gif differ diff --git a/images/readme/Boid5K_1.gif b/images/readme/Boid5K_1.gif new file mode 100644 index 0000000..95070ab Binary files /dev/null and b/images/readme/Boid5K_1.gif differ diff --git a/images/readme/Boid5K_2.gif b/images/readme/Boid5K_2.gif new file mode 100644 index 0000000..d164ed1 Binary files /dev/null and b/images/readme/Boid5K_2.gif differ diff 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a/profile/Vis_0/BinSearchComp/CIS565Proj1,Boid_1000000,BkSize_128,Vis_0,GCWidth_5,Nei_27,ShMem_0,Coherent,USort,ConstBoids.csv b/profile/Vis_0/BinSearchComp/CIS565Proj1,Boid_1000000,BkSize_128,Vis_0,GCWidth_5,Nei_27,ShMem_0,Coherent,USort,ConstBoids.csv new file mode 100644 index 0000000..af23e7d --- /dev/null +++ b/profile/Vis_0/BinSearchComp/CIS565Proj1,Boid_1000000,BkSize_128,Vis_0,GCWidth_5,Nei_27,ShMem_0,Coherent,USort,ConstBoids.csv @@ -0,0 +1,1028 @@ +,fps,duration,cudaTotal,kernComputeIndices-905,thrust::sort_by_key-909,kernResetIntBuffer-915,kernResetIntBuffer-916,kernIdentifyCellStartEnd-919,kernReshuffle-925,kernReshuffle-926,kernUpdateVelNeighborSearchCoherent-929,kernUpdatePos-934 +avg_1024,103.16,9.7297,8.70375,0.0697966,0.841056,0.00860906,0.00597519,0.0295964,0.0987699,0.10603,7.42524,0.118685 +max_1024,128.692,12.9639,10.9988,0.11104,3.10886,0.08192,0.034816,0.053152,0.11504,0.454656,9.26451,0.459584 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b/profile/Vis_0/BlockSizeCmp/CIS565Proj1,Boid_100000,BkSize_32,Vis_0,GCWidth_10,Nei_8,ShMem_0,Coherent,USort,ConstBoids.csv new file mode 100644 index 0000000..52e8267 --- /dev/null +++ b/profile/Vis_0/BlockSizeCmp/CIS565Proj1,Boid_100000,BkSize_32,Vis_0,GCWidth_10,Nei_8,ShMem_0,Coherent,USort,ConstBoids.csv @@ -0,0 +1,1028 @@ +,fps,duration,cudaTotal,kernComputeIndices-905,thrust::sort_by_key-909,kernResetIntBuffer-915,kernResetIntBuffer-916,kernIdentifyCellStartEnd-919,kernReshuffle-925,kernReshuffle-926,kernUpdateVelNeighborSearchCoherent-929,kernUpdatePos-934 +avg_1024,572.472,1.80524,1.29645,0.0125859,0.264067,0.00605313,0.00542856,0.0110258,0.0140428,0.0168203,0.953126,0.0132968 +max_1024,1227.63,3.38379,2.04816,0.067776,0.558464,0.063488,0.028064,0.045056,0.065952,0.03344,1.61766,0.282624 +min_1024,295.527,0.814575,1.07725,0.010656,0.2168,0.004096,0.004096,0.009248,0.011968,0.01488,0.77216,0.01056 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b/profile/Vis_0/BlockSizeCmp/CIS565Proj1,Boid_1000000,BkSize_128,Vis_0,GCWidth_10,Nei_8,ShMem_0,Coherent,USort,ConstBoids.csv new file mode 100644 index 0000000..534215f --- /dev/null +++ b/profile/Vis_0/BlockSizeCmp/CIS565Proj1,Boid_1000000,BkSize_128,Vis_0,GCWidth_10,Nei_8,ShMem_0,Coherent,USort,ConstBoids.csv @@ -0,0 +1,1028 @@ +,fps,duration,cudaTotal,kernComputeIndices-905,thrust::sort_by_key-909,kernResetIntBuffer-915,kernResetIntBuffer-916,kernIdentifyCellStartEnd-919,kernReshuffle-925,kernReshuffle-926,kernUpdateVelNeighborSearchCoherent-929,kernUpdatePos-934 +avg_1024,33.5914,29.7883,28.6141,0.0697025,0.944534,0.00757328,0.00561109,0.0289188,0.0964729,0.0992089,27.2459,0.116129 +max_1024,36.4984,35.1523,31.4902,0.084288,3.12502,0.043264,0.021408,0.045056,0.11808,0.458144,29.1158,0.50384 +min_1024,28.4476,27.3984,27.8118,0.065824,0.833664,0.006112,0.004064,0.02672,0.093152,0.095904,26.5107,0.111392 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b/profile/Vis_0/BlockSizeCmp/CIS565Proj1,Boid_1000000,BkSize_32,Vis_0,GCWidth_10,Nei_8,ShMem_0,Coherent,USort,ConstBoids.csv new file mode 100644 index 0000000..b34d905 --- /dev/null +++ b/profile/Vis_0/BlockSizeCmp/CIS565Proj1,Boid_1000000,BkSize_32,Vis_0,GCWidth_10,Nei_8,ShMem_0,Coherent,USort,ConstBoids.csv @@ -0,0 +1,1028 @@ +,fps,duration,cudaTotal,kernComputeIndices-905,thrust::sort_by_key-909,kernResetIntBuffer-915,kernResetIntBuffer-916,kernIdentifyCellStartEnd-919,kernReshuffle-925,kernReshuffle-926,kernUpdateVelNeighborSearchCoherent-929,kernUpdatePos-934 +avg_1024,30.3826,32.9281,31.7783,0.0824424,0.845362,0.00757084,0.00581197,0.069435,0.116704,0.119808,30.4173,0.113786 +max_1024,33.3877,36.377,34.1108,0.09808,2.63101,0.024544,0.020736,0.086048,0.137248,0.417792,31.9758,0.451776 +min_1024,27.4899,29.9512,31.0341,0.079872,0.748896,0.006144,0.004096,0.067584,0.112864,0.115552,29.7381,0.111072 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--git a/profile/Vis_0/BlockSizeCmp/CIS565Proj1,Boid_5000,BkSize_128,Vis_0,GCWidth_10,Nei_8,ShMem_0,Coherent,USort,ConstBoids.csv b/profile/Vis_0/BlockSizeCmp/CIS565Proj1,Boid_5000,BkSize_128,Vis_0,GCWidth_10,Nei_8,ShMem_0,Coherent,USort,ConstBoids.csv new file mode 100644 index 0000000..0fa4e14 --- /dev/null +++ b/profile/Vis_0/BlockSizeCmp/CIS565Proj1,Boid_5000,BkSize_128,Vis_0,GCWidth_10,Nei_8,ShMem_0,Coherent,USort,ConstBoids.csv @@ -0,0 +1,1028 @@ +,fps,duration,cudaTotal,kernComputeIndices-905,thrust::sort_by_key-909,kernResetIntBuffer-915,kernResetIntBuffer-916,kernIdentifyCellStartEnd-919,kernReshuffle-925,kernReshuffle-926,kernUpdateVelNeighborSearchCoherent-929,kernUpdatePos-934 +avg_1024,1305.28,0.822969,0.318965,0.00557231,0.21955,0.00528191,0.00502478,0.00534025,0.00588906,0.00581694,0.0610367,0.00545328 +max_1024,1821.66,2.39026,0.949152,0.020896,0.835552,0.04096,0.028704,0.022176,0.040736,0.026752,0.090592,0.036672 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b/profile/Vis_0/BoidSizeCmp/CIS565Proj1,Boid_100000,BkSize_128,Vis_0,GCWidth_10,Nei_8,ShMem_0,Coherent,USort,ConstBoids.csv new file mode 100644 index 0000000..cfd0886 --- /dev/null +++ b/profile/Vis_0/BoidSizeCmp/CIS565Proj1,Boid_100000,BkSize_128,Vis_0,GCWidth_10,Nei_8,ShMem_0,Coherent,USort,ConstBoids.csv @@ -0,0 +1,1028 @@ +,fps,duration,cudaTotal,kernComputeIndices-905,thrust::sort_by_key-909,kernResetIntBuffer-915,kernResetIntBuffer-916,kernIdentifyCellStartEnd-919,kernReshuffle-925,kernReshuffle-926,kernUpdateVelNeighborSearchCoherent-929,kernUpdatePos-934 +avg_1024,613.832,1.69437,1.2314,0.00880319,0.268922,0.00576794,0.00497991,0.007329,0.0135608,0.0146943,0.895376,0.0119631 +max_1024,1264,4.00232,1.95226,0.052416,0.726336,0.059392,0.02864,0.047744,0.04112,0.071616,1.62118,0.314336 +min_1024,249.855,0.791138,0.958464,0.006656,0.220128,0.004064,0.004064,0.006144,0.012256,0.0128,0.667648,0.008416 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b/profile/Vis_0/BoidSizeCmp/CIS565Proj1,Boid_1000000,BkSize_128,Vis_0,GCWidth_10,Nei_8,ShMem_0,Scattered,USort,ConstBoids.csv new file mode 100644 index 0000000..cfa760c --- /dev/null +++ b/profile/Vis_0/BoidSizeCmp/CIS565Proj1,Boid_1000000,BkSize_128,Vis_0,GCWidth_10,Nei_8,ShMem_0,Scattered,USort,ConstBoids.csv @@ -0,0 +1,1028 @@ +,fps,duration,cudaTotal,kernComputeIndices-836,thrust::sort_by_key-840,kernResetIntBuffer-846,kernResetIntBuffer-847,kernIdentifyCellStartEnd-850,kernUpdateVelNeighborSearchScattered-857,kernUpdatePos-862 +avg_1024,17.3854,57.5358,56.3707,0.0707558,0.843909,0.00765184,0.0059795,0.0293347,55.2962,0.116838 +max_1024,18.0765,67.1797,65.5259,0.088512,2.51728,0.038912,0.033088,0.051264,64.4353,0.508448 +min_1024,14.8855,55.3203,55.0114,0.067552,0.741376,0.006112,0.004096,0.0272,54.0251,0.1128 +512,17.213,58.0957,56.1392,0.067552,1.22806,0.00688,0.005792,0.028352,54.6864,0.116192 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b/profile/Vis_0/BoidSizeCmp/CIS565Proj1,Boid_150000,BkSize_128,Vis_0,GCWidth_10,Nei_8,ShMem_0,Scattered,USort,ConstBoids.csv new file mode 100644 index 0000000..a2428a2 --- /dev/null +++ b/profile/Vis_0/BoidSizeCmp/CIS565Proj1,Boid_150000,BkSize_128,Vis_0,GCWidth_10,Nei_8,ShMem_0,Scattered,USort,ConstBoids.csv @@ -0,0 +1,1028 @@ +,fps,duration,cudaTotal,kernComputeIndices-836,thrust::sort_by_key-840,kernResetIntBuffer-846,kernResetIntBuffer-847,kernIdentifyCellStartEnd-850,kernUpdateVelNeighborSearchScattered-857,kernUpdatePos-862 +avg_1024,338.735,3.00141,2.38533,0.016967,0.376779,0.00732259,0.00514381,0.00834491,1.95114,0.019627 +max_1024,510.341,6.83203,3.59046,0.097216,0.936,0.060832,0.031488,0.045056,3.01677,0.335872 +min_1024,146.369,1.95947,1.9912,0.012288,0.314368,0.005056,0.004064,0.00656,1.60106,0.014816 +512,374.406,2.6709,2.09526,0.012288,0.407552,0.007872,0.004416,0.008192,1.6384,0.016544 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a/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_100000,BkSize_128,Vis_0,GCWidth_10,Nei_8,ShMem_0,Coherent,USort,ConstBoids.csv b/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_100000,BkSize_128,Vis_0,GCWidth_10,Nei_8,ShMem_0,Coherent,USort,ConstBoids.csv new file mode 100644 index 0000000..cfd0886 --- /dev/null +++ b/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_100000,BkSize_128,Vis_0,GCWidth_10,Nei_8,ShMem_0,Coherent,USort,ConstBoids.csv @@ -0,0 +1,1028 @@ +,fps,duration,cudaTotal,kernComputeIndices-905,thrust::sort_by_key-909,kernResetIntBuffer-915,kernResetIntBuffer-916,kernIdentifyCellStartEnd-919,kernReshuffle-925,kernReshuffle-926,kernUpdateVelNeighborSearchCoherent-929,kernUpdatePos-934 +avg_1024,613.832,1.69437,1.2314,0.00880319,0.268922,0.00576794,0.00497991,0.007329,0.0135608,0.0146943,0.895376,0.0119631 +max_1024,1264,4.00232,1.95226,0.052416,0.726336,0.059392,0.02864,0.047744,0.04112,0.071616,1.62118,0.314336 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a/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_100000,BkSize_128,Vis_0,GCWidth_2,Nei_64,ShMem_0,Coherent,USort,ConstBoids.csv b/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_100000,BkSize_128,Vis_0,GCWidth_2,Nei_64,ShMem_0,Coherent,USort,ConstBoids.csv new file mode 100644 index 0000000..b05b3c7 --- /dev/null +++ b/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_100000,BkSize_128,Vis_0,GCWidth_2,Nei_64,ShMem_0,Coherent,USort,ConstBoids.csv @@ -0,0 +1,1028 @@ +,fps,duration,cudaTotal,kernComputeIndices-905,thrust::sort_by_key-909,kernResetIntBuffer-915,kernResetIntBuffer-916,kernIdentifyCellStartEnd-919,kernReshuffle-925,kernReshuffle-926,kernUpdateVelNeighborSearchCoherent-929,kernUpdatePos-934 +avg_1024,744.518,1.39892,0.919129,0.00888756,0.274281,0.0118885,0.0109816,0.0174519,0.0165047,0.0151436,0.55149,0.0125002 +max_1024,1575.08,3.78247,1.79376,0.044512,0.749216,0.081312,0.057344,0.057376,0.042304,0.031872,1.37712,0.026656 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a/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_100000,BkSize_128,Vis_0,GCWidth_3,Nei_27,ShMem_0,Coherent,USort,ConstBoids.csv b/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_100000,BkSize_128,Vis_0,GCWidth_3,Nei_27,ShMem_0,Coherent,USort,ConstBoids.csv new file mode 100644 index 0000000..577f2c8 --- /dev/null +++ b/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_100000,BkSize_128,Vis_0,GCWidth_3,Nei_27,ShMem_0,Coherent,USort,ConstBoids.csv @@ -0,0 +1,1028 @@ +,fps,duration,cudaTotal,kernComputeIndices-905,thrust::sort_by_key-909,kernResetIntBuffer-915,kernResetIntBuffer-916,kernIdentifyCellStartEnd-919,kernReshuffle-925,kernReshuffle-926,kernUpdateVelNeighborSearchCoherent-929,kernUpdatePos-934 +avg_1024,615.283,1.72256,1.27836,0.00818444,0.265188,0.00888831,0.00874781,0.0101772,0.0158393,0.0154333,0.933515,0.0123886 +max_1024,1386.59,3.26855,2.41664,0.045344,0.615776,0.050624,0.057344,0.024288,0.041984,0.04016,2.03872,0.286304 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a/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_100000,BkSize_128,Vis_0,GCWidth_4,Nei_27,ShMem_0,Coherent,USort,ConstBoids.csv b/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_100000,BkSize_128,Vis_0,GCWidth_4,Nei_27,ShMem_0,Coherent,USort,ConstBoids.csv new file mode 100644 index 0000000..f9cbadf --- /dev/null +++ b/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_100000,BkSize_128,Vis_0,GCWidth_4,Nei_27,ShMem_0,Coherent,USort,ConstBoids.csv @@ -0,0 +1,1028 @@ +,fps,duration,cudaTotal,kernComputeIndices-905,thrust::sort_by_key-909,kernResetIntBuffer-915,kernResetIntBuffer-916,kernIdentifyCellStartEnd-919,kernReshuffle-925,kernReshuffle-926,kernUpdateVelNeighborSearchCoherent-929,kernUpdatePos-934 +avg_1024,600.166,1.73114,1.25641,0.00827238,0.255334,0.00705225,0.00662253,0.00825216,0.0144533,0.0152,0.929599,0.0116242 +max_1024,1286.84,4.896,2.03254,0.024576,0.62816,0.064672,0.049376,0.084288,0.06608,0.030432,1.6647,0.350144 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a/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_100000,BkSize_128,Vis_0,GCWidth_6,Nei_27,ShMem_0,Coherent,USort,ConstBoids.csv b/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_100000,BkSize_128,Vis_0,GCWidth_6,Nei_27,ShMem_0,Coherent,USort,ConstBoids.csv new file mode 100644 index 0000000..cf48805 --- /dev/null +++ b/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_100000,BkSize_128,Vis_0,GCWidth_6,Nei_27,ShMem_0,Coherent,USort,ConstBoids.csv @@ -0,0 +1,1028 @@ +,fps,duration,cudaTotal,kernComputeIndices-905,thrust::sort_by_key-909,kernResetIntBuffer-915,kernResetIntBuffer-916,kernIdentifyCellStartEnd-919,kernReshuffle-925,kernReshuffle-926,kernUpdateVelNeighborSearchCoherent-929,kernUpdatePos-934 +avg_1024,694.273,1.50248,1.00773,0.00858434,0.256004,0.00597947,0.00558541,0.00730513,0.0140511,0.0149988,0.684659,0.0105582 +max_1024,1447.35,3.94177,1.99066,0.060224,0.798688,0.063584,0.021344,0.024064,0.056928,0.05584,1.69053,0.065536 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a/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_100000,BkSize_128,Vis_0,GCWidth_8,Nei_8,ShMem_0,Coherent,USort,ConstBoids.csv b/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_100000,BkSize_128,Vis_0,GCWidth_8,Nei_8,ShMem_0,Coherent,USort,ConstBoids.csv new file mode 100644 index 0000000..2847ae8 --- /dev/null +++ b/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_100000,BkSize_128,Vis_0,GCWidth_8,Nei_8,ShMem_0,Coherent,USort,ConstBoids.csv @@ -0,0 +1,1028 @@ +,fps,duration,cudaTotal,kernComputeIndices-905,thrust::sort_by_key-909,kernResetIntBuffer-915,kernResetIntBuffer-916,kernIdentifyCellStartEnd-919,kernReshuffle-925,kernReshuffle-926,kernUpdateVelNeighborSearchCoherent-929,kernUpdatePos-934 +avg_1024,540.994,1.90354,1.38772,0.00895031,0.262703,0.00642206,0.00546653,0.00723641,0.0136414,0.0147787,1.05591,0.0126161 +max_1024,1009.86,3.20825,2.15648,0.033408,0.724992,0.045056,0.028928,0.047104,0.029152,0.053376,1.82931,0.271136 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a/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_1000000,BkSize_128,Vis_0,GCWidth_10,Nei_8,ShMem_0,Coherent,USort,ConstBoids.csv b/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_1000000,BkSize_128,Vis_0,GCWidth_10,Nei_8,ShMem_0,Coherent,USort,ConstBoids.csv new file mode 100644 index 0000000..534215f --- /dev/null +++ b/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_1000000,BkSize_128,Vis_0,GCWidth_10,Nei_8,ShMem_0,Coherent,USort,ConstBoids.csv @@ -0,0 +1,1028 @@ +,fps,duration,cudaTotal,kernComputeIndices-905,thrust::sort_by_key-909,kernResetIntBuffer-915,kernResetIntBuffer-916,kernIdentifyCellStartEnd-919,kernReshuffle-925,kernReshuffle-926,kernUpdateVelNeighborSearchCoherent-929,kernUpdatePos-934 +avg_1024,33.5914,29.7883,28.6141,0.0697025,0.944534,0.00757328,0.00561109,0.0289188,0.0964729,0.0992089,27.2459,0.116129 +max_1024,36.4984,35.1523,31.4902,0.084288,3.12502,0.043264,0.021408,0.045056,0.11808,0.458144,29.1158,0.50384 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a/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_1000000,BkSize_128,Vis_0,GCWidth_3,Nei_27,ShMem_0,Coherent,USort,ConstBoids.csv b/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_1000000,BkSize_128,Vis_0,GCWidth_3,Nei_27,ShMem_0,Coherent,USort,ConstBoids.csv new file mode 100644 index 0000000..064bc51 --- /dev/null +++ b/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_1000000,BkSize_128,Vis_0,GCWidth_3,Nei_27,ShMem_0,Coherent,USort,ConstBoids.csv @@ -0,0 +1,1028 @@ +,fps,duration,cudaTotal,kernComputeIndices-905,thrust::sort_by_key-909,kernResetIntBuffer-915,kernResetIntBuffer-916,kernIdentifyCellStartEnd-919,kernReshuffle-925,kernReshuffle-926,kernUpdateVelNeighborSearchCoherent-929,kernUpdatePos-934 +avg_1024,50.2442,19.9376,18.7803,0.0701488,0.877415,0.0109799,0.00865663,0.0346423,0.104585,0.102464,17.4558,0.115609 +max_1024,59.061,27.1211,25.9604,0.09712,1.59494,0.029664,0.024096,0.059328,0.12224,0.34512,24.6478,0.397664 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b/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_1000000,BkSize_128,Vis_0,GCWidth_4,Nei_27,ShMem_0,Coherent,USort,ConstBoids.csv new file mode 100644 index 0000000..b3c4d72 --- /dev/null +++ b/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_1000000,BkSize_128,Vis_0,GCWidth_4,Nei_27,ShMem_0,Coherent,USort,ConstBoids.csv @@ -0,0 +1,1028 @@ +,fps,duration,cudaTotal,kernComputeIndices-905,thrust::sort_by_key-909,kernResetIntBuffer-915,kernResetIntBuffer-916,kernIdentifyCellStartEnd-919,kernReshuffle-925,kernReshuffle-926,kernUpdateVelNeighborSearchCoherent-929,kernUpdatePos-934 +avg_1024,50.1481,19.9723,18.8099,0.0699937,0.88795,0.00898281,0.00671222,0.0309523,0.0998673,0.100916,17.4901,0.114458 +max_1024,56.5059,23.0488,21.023,0.092704,2.43859,0.038816,0.022592,0.05024,0.120768,0.433792,19.4384,0.134144 +min_1024,43.3862,17.6973,18.1729,0.065888,0.790528,0.007008,0.00592,0.0288,0.096608,0.096832,16.9293,0.111392 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b/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_1000000,BkSize_128,Vis_0,GCWidth_5,Nei_27,ShMem_0,Coherent,USort,ConstBoids.csv new file mode 100644 index 0000000..ae3dea6 --- /dev/null +++ b/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_1000000,BkSize_128,Vis_0,GCWidth_5,Nei_27,ShMem_0,Coherent,USort,ConstBoids.csv @@ -0,0 +1,1028 @@ +,fps,duration,cudaTotal,kernComputeIndices-905,thrust::sort_by_key-909,kernResetIntBuffer-915,kernResetIntBuffer-916,kernIdentifyCellStartEnd-919,kernReshuffle-925,kernReshuffle-926,kernUpdateVelNeighborSearchCoherent-929,kernUpdatePos-934 +avg_1024,103.064,9.74495,8.6879,0.0699586,0.857607,0.00906256,0.00595553,0.0297373,0.0985658,0.10195,7.3924,0.122669 +max_1024,134.418,13.5801,10.9811,0.124256,2.12406,0.075552,0.022144,0.066016,0.1136,0.446848,9.13968,0.477184 +min_1024,73.6373,7.43945,8.21981,0.065536,0.738944,0.006624,0.004448,0.027648,0.096224,0.096416,7.05523,0.11216 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b/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_1000000,BkSize_128,Vis_0,GCWidth_6,Nei_27,ShMem_0,Coherent,USort,ConstBoids.csv new file mode 100644 index 0000000..a0feed0 --- /dev/null +++ b/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_1000000,BkSize_128,Vis_0,GCWidth_6,Nei_27,ShMem_0,Coherent,USort,ConstBoids.csv @@ -0,0 +1,1028 @@ +,fps,duration,cudaTotal,kernComputeIndices-905,thrust::sort_by_key-909,kernResetIntBuffer-915,kernResetIntBuffer-916,kernIdentifyCellStartEnd-919,kernReshuffle-925,kernReshuffle-926,kernUpdateVelNeighborSearchCoherent-929,kernUpdatePos-934 +avg_1024,59.8882,16.7292,15.6001,0.0701553,0.951355,0.00752688,0.00580347,0.0291874,0.0976586,0.0990625,14.2218,0.117513 +max_1024,74.5161,20.5195,17.6596,0.090528,2.8329,0.023328,0.022528,0.047104,0.113152,0.39904,16.3267,0.494112 +min_1024,48.7341,13.4199,14.981,0.066496,0.839936,0.006176,0.004096,0.027296,0.094624,0.096192,13.6779,0.111136 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b/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_1000000,BkSize_128,Vis_0,GCWidth_8,Nei_8,ShMem_0,Coherent,USort,ConstBoids.csv new file mode 100644 index 0000000..949e3ba --- /dev/null +++ b/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_1000000,BkSize_128,Vis_0,GCWidth_8,Nei_8,ShMem_0,Coherent,USort,ConstBoids.csv @@ -0,0 +1,1028 @@ +,fps,duration,cudaTotal,kernComputeIndices-905,thrust::sort_by_key-909,kernResetIntBuffer-915,kernResetIntBuffer-916,kernIdentifyCellStartEnd-919,kernReshuffle-925,kernReshuffle-926,kernUpdateVelNeighborSearchCoherent-929,kernUpdatePos-934 +avg_1024,32.1578,31.1128,29.9054,0.0700682,0.956986,0.00754881,0.0056525,0.0288603,0.0965178,0.0982233,28.527,0.114593 +max_1024,34.9679,33.832,31.915,0.104448,1.6992,0.024064,0.020512,0.054816,0.115072,0.117024,30.4625,0.354304 +min_1024,29.5578,28.5977,29.0105,0.067584,0.844096,0.006144,0.004096,0.026688,0.094208,0.095232,27.7076,0.111456 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b/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_5000,BkSize_128,Vis_0,GCWidth_10,Nei_8,ShMem_0,Coherent,USort,ConstBoids.csv new file mode 100644 index 0000000..0fa4e14 --- /dev/null +++ b/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_5000,BkSize_128,Vis_0,GCWidth_10,Nei_8,ShMem_0,Coherent,USort,ConstBoids.csv @@ -0,0 +1,1028 @@ +,fps,duration,cudaTotal,kernComputeIndices-905,thrust::sort_by_key-909,kernResetIntBuffer-915,kernResetIntBuffer-916,kernIdentifyCellStartEnd-919,kernReshuffle-925,kernReshuffle-926,kernUpdateVelNeighborSearchCoherent-929,kernUpdatePos-934 +avg_1024,1305.28,0.822969,0.318965,0.00557231,0.21955,0.00528191,0.00502478,0.00534025,0.00588906,0.00581694,0.0610367,0.00545328 +max_1024,1821.66,2.39026,0.949152,0.020896,0.835552,0.04096,0.028704,0.022176,0.040736,0.026752,0.090592,0.036672 +min_1024,418.365,0.54895,0.274336,0.004288,0.192416,0.004064,0.004064,0.004096,0.004288,0.004448,0.04096,0.004128 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a/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_5000,BkSize_128,Vis_0,GCWidth_3,Nei_27,ShMem_0,Coherent,USort,ConstBoids.csv b/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_5000,BkSize_128,Vis_0,GCWidth_3,Nei_27,ShMem_0,Coherent,USort,ConstBoids.csv new file mode 100644 index 0000000..b1076f9 --- /dev/null +++ b/profile/Vis_0/GridCellWidthCmp/CIS565Proj1,Boid_5000,BkSize_128,Vis_0,GCWidth_3,Nei_27,ShMem_0,Coherent,USort,ConstBoids.csv @@ -0,0 +1,1028 @@ +,fps,duration,cudaTotal,kernComputeIndices-905,thrust::sort_by_key-909,kernResetIntBuffer-915,kernResetIntBuffer-916,kernIdentifyCellStartEnd-919,kernReshuffle-925,kernReshuffle-926,kernUpdateVelNeighborSearchCoherent-929,kernUpdatePos-934 +avg_1024,1168.29,0.922673,0.388684,0.00568378,0.23652,0.00894294,0.00886709,0.00626147,0.00602863,0.00656978,0.104371,0.00543863 +max_1024,1670.81,2.58899,1.65683,0.03072,1.5097,0.065536,0.081312,0.044736,0.022368,0.021344,0.21056,0.02224 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b/profile/Vis_1/BoidSizeCmp/CIS565Proj1,Boid_20000,BkSize_128,Vis_1,GCWidth_10,Nei_8,ShMem_0,Scattered,USort,ConstBoids.csv new file mode 100644 index 0000000..1f72f57 --- /dev/null +++ b/profile/Vis_1/BoidSizeCmp/CIS565Proj1,Boid_20000,BkSize_128,Vis_1,GCWidth_10,Nei_8,ShMem_0,Scattered,USort,ConstBoids.csv @@ -0,0 +1,1028 @@ +,fps,duration,cudaTotal,kernComputeIndices-836,thrust::sort_by_key-840,kernResetIntBuffer-846,kernResetIntBuffer-847,kernIdentifyCellStartEnd-850,kernUpdateVelNeighborSearchScattered-857,kernUpdatePos-862 +avg_1024,509.04,2.03004,0.498364,0.00664019,0.251513,0.00527263,0.00499025,0.00555938,0.218278,0.00611038 +max_1024,677.921,6.3645,0.845728,0.024576,0.591872,0.01568,0.02048,0.020896,0.249312,0.021536 +min_1024,157.121,1.4751,0.438528,0.004768,0.219136,0.004096,0.004064,0.004288,0.186368,0.004928 +512,569.363,1.75635,0.456096,0.007488,0.233472,0.0048,0.0056,0.00464,0.19408,0.006016 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a/profile/Vis_1/BoidSizeCmp/CIS565Proj1,Boid_500000,BkSize_128,Vis_1,GCWidth_10,Nei_8,ShMem_0,Coherent,USort,ConstBoids.csv b/profile/Vis_1/BoidSizeCmp/CIS565Proj1,Boid_500000,BkSize_128,Vis_1,GCWidth_10,Nei_8,ShMem_0,Coherent,USort,ConstBoids.csv new file mode 100644 index 0000000..d04a776 --- /dev/null +++ b/profile/Vis_1/BoidSizeCmp/CIS565Proj1,Boid_500000,BkSize_128,Vis_1,GCWidth_10,Nei_8,ShMem_0,Coherent,USort,ConstBoids.csv @@ -0,0 +1,1028 @@ +,fps,duration,cudaTotal,kernComputeIndices-905,thrust::sort_by_key-909,kernResetIntBuffer-915,kernResetIntBuffer-916,kernIdentifyCellStartEnd-919,kernReshuffle-925,kernReshuffle-926,kernUpdateVelNeighborSearchCoherent-929,kernUpdatePos-934 +avg_1024,98.7896,10.1394,8.03323,0.0387713,0.61435,0.00803303,0.00585438,0.0165496,0.0508889,0.0540457,7.18587,0.0588711 +max_1024,106.767,12.752,9.05389,0.202112,1.3783,0.024352,0.022528,0.03392,0.067456,0.07168,7.49987,0.080768 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b/profile/Vis_1/BoidSizeCmp/CIS565Proj1,Boid_500000,BkSize_128,Vis_1,GCWidth_10,Nei_8,ShMem_0,Scattered,USort,ConstBoids.csv new file mode 100644 index 0000000..0000905 --- /dev/null +++ b/profile/Vis_1/BoidSizeCmp/CIS565Proj1,Boid_500000,BkSize_128,Vis_1,GCWidth_10,Nei_8,ShMem_0,Scattered,USort,ConstBoids.csv @@ -0,0 +1,1028 @@ +,fps,duration,cudaTotal,kernComputeIndices-836,thrust::sort_by_key-840,kernResetIntBuffer-846,kernResetIntBuffer-847,kernIdentifyCellStartEnd-850,kernUpdateVelNeighborSearchScattered-857,kernUpdatePos-862 +avg_1024,61.3226,16.3354,14.0764,0.03863,0.61466,0.00727031,0.00551569,0.0165635,13.3346,0.0591973 +max_1024,65.2645,22.751,17.0749,0.054496,2.98144,0.0248,0.022368,0.044864,14.6011,0.076 +min_1024,43.9542,15.3223,13.6581,0.035552,0.542368,0.006144,0.004064,0.015008,12.9534,0.057344 +512,57.1301,17.5039,15.0693,0.038368,0.563872,0.007744,0.004544,0.016384,14.379,0.059392 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Block size used for CUDA kernel launch. */ -#define blockSize 128 +#define blockSize PFM_ANA_blockSize +//#define blockSize 32 //16 //32 //64 //1024 //512 //256 //128 + +/* Start my own definition */ +// Clamp simply on each component. +#define clampFunc(dst, min, max) dst = glm::length(dst) <= max ? dst : glm::normalize(dst) * max; +//#define clampFunc(dst, min, max) dst = glm::clamp(dst, min, max) + +#define blockSizePerDim blockSize // 8 + +__constant__ int dev_gridCellCountPtr[1]; + +/* End my own definition */ // LOOK-1.2 Parameters for the boids algorithm. // These worked well in our reference implementation. @@ -72,6 +84,8 @@ glm::vec3 *dev_vel2; // LOOK-2.1 - these are NOT allocated for you. You'll have to set up the thrust // pointers on your own too. +glm::vec3* dev_pos_reshuffle; +glm::vec3* dev_vel_reshuffle; // For efficient sorting and the uniform grid. These should always be parallel. int *dev_particleArrayIndices; // What index in dev_pos and dev_velX represents this particle? @@ -83,7 +97,7 @@ thrust::device_ptr dev_thrust_particleGridIndices; int *dev_gridCellStartIndices; // What part of dev_particleArrayIndices belongs int *dev_gridCellEndIndices; // to this cell? -// TODO-2.3 - consider what additional buffers you might need to reshuffle +// DONE-2.3 - consider what additional buffers you might need to reshuffle // the position and velocity data to be coherent within cells. // LOOK-2.1 - Grid parameters based on simulation parameters. @@ -157,7 +171,15 @@ void Boids::initSimulation(int N) { checkCUDAErrorWithLine("kernGenerateRandomPosArray failed!"); // LOOK-2.1 computing grid params +#if !GRID_LOOPING_OPTIMIZATION +#if !USE_HALF_SIZE_OF_CELL gridCellWidth = 2.0f * std::max(std::max(rule1Distance, rule2Distance), rule3Distance); +#else // USE_HALF_SIZE_OF_CELL + gridCellWidth = std::max(std::max(rule1Distance, rule2Distance), rule3Distance); +#endif // USE_HALF_SIZE_OF_CELL +#else //GRID_LOOPING_OPTIMIZATION + gridCellWidth = GRID_LOOPING_WIDTH;//std::min(std::min(rule1Distance, rule2Distance), rule3Distance); // Not appropriate? +#endif // GRID_LOOPING_OPTIMIZATION int halfSideCount = (int)(scene_scale / gridCellWidth) + 1; gridSideCount = 2 * halfSideCount; @@ -168,8 +190,49 @@ void Boids::initSimulation(int N) { gridMinimum.y -= halfGridWidth; gridMinimum.z -= halfGridWidth; - // TODO-2.1 TODO-2.3 - Allocate additional buffers here. + // DONE-2.1 DONE-2.3 - Allocate additional buffers here. + + // Start implementation 2.1 + cudaMalloc((void**)&dev_particleArrayIndices, N * sizeof(int)); // boid index -> sorted pos + vel index + cudaMalloc((void**)&dev_particleGridIndices, N * sizeof(int)); // boid index -> grid cell index + cudaMalloc((void**)&dev_gridCellStartIndices, gridCellCount * sizeof(int)); // grid cell index -> start boid index + cudaMalloc((void**)&dev_gridCellEndIndices, gridCellCount * sizeof(int)); // grid cell index -> end boid index + + dev_thrust_particleArrayIndices = thrust::device_ptr(dev_particleArrayIndices); + dev_thrust_particleGridIndices = thrust::device_ptr(dev_particleGridIndices); + // End implementation 2.1 + + // Start implementation 2.3 + cudaMalloc((void**)&dev_pos_reshuffle, N * sizeof(glm::vec3)); + cudaMalloc((void**)&dev_vel_reshuffle, N * sizeof(glm::vec3)); + // End implementation 2.3 + + cudaMemcpyToSymbol(dev_gridCellCountPtr, &gridCellCount, sizeof(int)); + cudaDeviceSynchronize(); + +#if ENABLE_PROFILE_LOG + float idxDiff = std::max(std::max(rule1Distance, rule2Distance), rule3Distance) * gridInverseCellWidth; + int sideSearchSize = static_cast(1.25 + idxDiff) - static_cast(1.25 - idxDiff) + 1; + + utilityCore::ProfileLog::get().addKwArg("Boid", numObjects); + utilityCore::ProfileLog::get().addKwArg("BkSize", blockSize); + utilityCore::ProfileLog::get().addKwArg("Vis", PFM_ANA_VISUALIZE); + utilityCore::ProfileLog::get().addKwArg("GCWidth", gridCellWidth); + utilityCore::ProfileLog::get().addKwArg("Nei", sideSearchSize * sideSearchSize * sideSearchSize); + utilityCore::ProfileLog::get().addKwArg("ShMem", USE_SHARED_MEMORY); + + utilityCore::ProfileLog::get().addArg((PFM_ANA_UNIFORM_GRID > 0 ? (PFM_ANA_COHERENT_GRID > 0 ? "Coherent" : "Scattered") : "Naive")); + utilityCore::ProfileLog::get().addArg((USE_STABLE_SORT > 0 ? "SSort" : "USort")); + utilityCore::ProfileLog::get().addArg((IDENTIFY_START_END_BY_BINARY_SEARCH > 0 ? "BinSearchGridCell" : "ConstBoids")); + + //float startTime = 1500.f; + int startFrame = 512; + int accFrameCount = 1024; + int frameInterval = 1; + + utilityCore::ProfileLog::get().initProfile(std::string("../profile/") + (PFM_ANA_VISUALIZE > 0 ? "Vis_1/" : "Vis_0/") + "CIS565Proj1", "end", startFrame, accFrameCount, frameInterval, std::ios_base::out); +#endif ENABLE_PROFILE_LOG } @@ -233,11 +296,63 @@ __device__ glm::vec3 computeVelocityChange(int N, int iSelf, const glm::vec3 *po // Rule 1: boids fly towards their local perceived center of mass, which excludes themselves // Rule 2: boids try to stay a distance d away from each other // Rule 3: boids try to match the speed of surrounding boids - return glm::vec3(0.0f, 0.0f, 0.0f); + + // return glm::vec3(0.0f, 0.0f, 0.0f); + + // Start implementation + glm::vec3 dVel(0.f, 0.f, 0.f); + + glm::vec3 perceivedCenter(0.f, 0.f, 0.f); + glm::vec3 collisionP(0.f, 0.f, 0.f); + glm::vec3 perceivedVel(0.f, 0.f, 0.f); + + int rule1Neighbor = 0, rule3Neighbor = 0; + + for (int i = 0; i < N; ++i) { + glm::vec3 diff = pos[i] - pos[iSelf]; + float distance = glm::length(diff); + if (i != iSelf) { + // Rule 1: boids fly towards their local perceived center of mass, which excludes themselves + if (distance < rule1Distance) { + perceivedCenter += pos[i]; + ++rule1Neighbor; + } + + // Rule 2: boids try to stay a distance d away from each other + if (distance < rule2Distance) { + collisionP -= diff; + } + + // Rule 3: boids try to match the speed of surrounding boids + if (distance < rule3Distance) { + perceivedVel += vel[i]; + ++rule3Neighbor; + } + } + } + + if (rule1Neighbor > 0) { + perceivedCenter /= rule1Neighbor; + dVel += (perceivedCenter - pos[iSelf]) * rule1Scale; + } + + dVel += collisionP * rule2Scale; + + if (rule3Neighbor > 0) { + perceivedVel /= rule3Neighbor; +#if !USE_RULE3_FROM_CONARD_PARKER + dVel += perceivedVel * rule3Scale; // From instruction +#else // USE_RULE3_FROM_CONARD_PARKER + dVel += (perceivedVel - vel[iSelf]) * rule3Scale; // From Conard Parker's, maybe it runs much better than the instructions' now? +#endif // USE_RULE3_FROM_CONARD_PARKER + } + // End implementation + + return dVel; } /** -* TODO-1.2 implement basic flocking +* DONE-1.2 implement basic flocking * For each of the `N` bodies, update its position based on its current velocity. */ __global__ void kernUpdateVelocityBruteForce(int N, glm::vec3 *pos, @@ -245,6 +360,23 @@ __global__ void kernUpdateVelocityBruteForce(int N, glm::vec3 *pos, // Compute a new velocity based on pos and vel1 // Clamp the speed // Record the new velocity into vel2. Question: why NOT vel1? + + // Answer: Because the new velocity is related to the velocities of the other boids. If you change vel1, it may change the result of the new velocity. + + // Start implementation + int index = threadIdx.x + (blockIdx.x * blockDim.x); + if (index >= N) { + return; + } + + // Compute a new velocity based on pos and vel1 + glm::vec3 dVel = computeVelocityChange(N, index, pos, vel1); + vel2[index] = vel1[index] + dVel; + + // Clamp the speed + clampFunc(vel2[index], -maxSpeed, maxSpeed); + + // End implementation } /** @@ -285,10 +417,27 @@ __device__ int gridIndex3Dto1D(int x, int y, int z, int gridResolution) { __global__ void kernComputeIndices(int N, int gridResolution, glm::vec3 gridMin, float inverseCellWidth, glm::vec3 *pos, int *indices, int *gridIndices) { - // TODO-2.1 + // DONE-2.1 // - Label each boid with the index of its grid cell. // - Set up a parallel array of integer indices as pointers to the actual // boid data in pos and vel1/vel2 + + // Start implementation + int index = (blockIdx.x * blockDim.x) + threadIdx.x; + if (index >= N) { + return; + } + + // Init sorted boid index + indices[index] = index; + + glm::vec3 posLocal = pos[index] - gridMin; + glm::vec3 idxLocal = posLocal * inverseCellWidth; + int x = idxLocal.x, y = idxLocal.y, z = idxLocal.z; + int indexCell = gridIndex3Dto1D(x, y, z, gridResolution); + + gridIndices[index] = indexCell; + // End implementation } // LOOK-2.1 Consider how this could be useful for indicating that a cell @@ -300,12 +449,82 @@ __global__ void kernResetIntBuffer(int N, int *intBuffer, int value) { } } -__global__ void kernIdentifyCellStartEnd(int N, int *particleGridIndices, - int *gridCellStartIndices, int *gridCellEndIndices) { - // TODO-2.1 +__global__ void kernIdentifyCellStartEnd(int N, int* particleGridIndices, + int* gridCellStartIndices, int* gridCellEndIndices) { + // DONE-2.1 // Identify the start point of each cell in the gridIndices array. // This is basically a parallel unrolling of a loop that goes // "this index doesn't match the one before it, must be a new cell!" + + // Start implementation + +#if !IDENTIFY_START_END_BY_BINARY_SEARCH + int index = (blockIdx.x * blockDim.x) + threadIdx.x; + if (index >= N) { + return; + } + + int prevIdx = imax(0, index - 1); + int nextIdx = imin(N - 1, index + 1); + + int indexCell = particleGridIndices[index]; + int prevIndexCell = particleGridIndices[prevIdx]; + int nextIndexCell = particleGridIndices[nextIdx]; + + if (index == 0 || indexCell != prevIndexCell) { + gridCellStartIndices[indexCell] = index; + } + if (index == N - 1 || indexCell != nextIndexCell) { + gridCellEndIndices[indexCell] = index + 1; + } + + //if (indexCell >= dev_gridCellCountPtr[0]) { + // return; + //} + //for (int i = 0; i < N; ++i) { + // if (particleGridIndices[i] == indexCell && gridCellStartIndices[indexCell] == -1) { + // gridCellStartIndices[indexCell] = i; + // } + // if (particleGridIndices[i] > indexCell && gridCellStartIndices[indexCell] != -1) { + // gridCellEndIndices[indexCell] = i; + // break; + // } + //} +#else // IDENTIFY_START_END_BY_BINARY_SEARCH + int indexCell = (blockIdx.x * blockDim.x) + threadIdx.x; + // Can we use CPU global variable in CUDA device scpoe? No? + //if (indexCell >= gridCellCount) { + // return; + //} + if (indexCell >= dev_gridCellCountPtr[0]) { + return; + } + + int initLeft = 0, initRight = N - 1; + while (initLeft < initRight) { + int mid = initLeft + ((initRight - initLeft) >> 1); + int midCell = particleGridIndices[mid]; + if (midCell < indexCell) { + initLeft = mid + 1; + } + else { + initRight = mid; + } + } + if (particleGridIndices[initLeft] != indexCell) { + return; + } + while (initLeft >= 0 && particleGridIndices[initLeft] == indexCell) { + --initLeft; + } + gridCellStartIndices[indexCell] = initLeft + 1; + while (initRight < N && particleGridIndices[initRight] == indexCell) { + ++initRight; + } + gridCellEndIndices[indexCell] = initRight; +#endif // IDENTIFY_START_END_BY_BINARY_SEARCH + + // End implementation } __global__ void kernUpdateVelNeighborSearchScattered( @@ -314,7 +533,7 @@ __global__ void kernUpdateVelNeighborSearchScattered( int *gridCellStartIndices, int *gridCellEndIndices, int *particleArrayIndices, glm::vec3 *pos, glm::vec3 *vel1, glm::vec3 *vel2) { - // TODO-2.1 - Update a boid's velocity using the uniform grid to reduce + // DONE-2.1 - Update a boid's velocity using the uniform grid to reduce // the number of boids that need to be checked. // - Identify the grid cell that this particle is in // - Identify which cells may contain neighbors. This isn't always 8. @@ -322,6 +541,121 @@ __global__ void kernUpdateVelNeighborSearchScattered( // - Access each boid in the cell and compute velocity change from // the boids rules, if this boid is within the neighborhood distance. // - Clamp the speed change before putting the new speed in vel2 + + // Start implementation + int index = (blockIdx.x * blockDim.x) + threadIdx.x; + if (index >= N) { + return; + } + int boidIndex = particleArrayIndices[index]; + glm::vec3 posOfIndex = pos[boidIndex]; + + // 2.1.1 Identify the cell + glm::vec3 posLocal = posOfIndex - gridMin; + glm::vec3 idxLocal = posLocal * inverseCellWidth; + //int x = idxLocal.x, y = idxLocal.y, z = idxLocal.z; + //int indexCell = gridIndex3Dto1D(x, y, z, gridResolution); + + // 2.1.2 Identify which cells may contain neighbors. +#if !GRID_LOOPING_OPTIMIZATION +#if !USE_HALF_SIZE_OF_CELL + int minX = idxLocal.x - 0.5f, minY = idxLocal.y - 0.5f, minZ = idxLocal.z - 0.5f; + int maxX = idxLocal.x + 0.5f, maxY = idxLocal.y + 0.5f, maxZ = idxLocal.z + 0.5f; +#else // USE_HALF_SIZE_OF_CELL + int minX = idxLocal.x - 1, minY = idxLocal.y - 1, minZ = idxLocal.z - 1; + int maxX = idxLocal.x + 1, maxY = idxLocal.y + 1, maxZ = idxLocal.z + 1; +#endif // USE_HALF_SIZE_OF_CELL +#else // GRID_LOOPING_OPTIMIZATION + float searchRadius = imax(rule1Distance, imax(rule2Distance, rule3Distance)); + float searchDiffIdx = searchRadius * inverseCellWidth; + int minX = idxLocal.x - searchDiffIdx, minY = idxLocal.y - searchDiffIdx, minZ = idxLocal.z - searchDiffIdx; + int maxX = idxLocal.x + searchDiffIdx, maxY = idxLocal.y + searchDiffIdx, maxZ = idxLocal.z + searchDiffIdx; +#endif // GRID_LOOPING_OPTIMIZATION + + minX = imax(minX, 0); + maxX = imin(maxX, gridResolution - 1); + minY = imax(minY, 0); + maxY = imin(maxY, gridResolution - 1); + minZ = imax(minZ, 0); + maxZ = imin(maxZ, gridResolution - 1); + + // 2.1.3 For each cell, ... + glm::vec3 dVel(0.f, 0.f, 0.f); + + glm::vec3 perceivedCenter(0.f, 0.f, 0.f); + glm::vec3 collisionP(0.f, 0.f, 0.f); + glm::vec3 perceivedVel(0.f, 0.f, 0.f); + + int rule1Neighbor = 0, rule3Neighbor = 0; + +#if !FOR_LOOP_XYZ + for (int z1 = minZ; z1 <= maxZ; ++z1) { + for (int y1 = minY; y1 <= maxY; ++y1) { + for (int x1 = minX; x1 <= maxX; ++x1) { +#else // FOR_LOOP_XYZ + for (int x1 = minX; x1 <= maxX; ++x1) { + for (int y1 = minY; y1 <= maxY; ++y1) { + for (int z1 = minZ; z1 <= maxZ; ++z1) { +#endif // FOR_LOOP_XYZ + int indexC1 = gridIndex3Dto1D(x1, y1, z1, gridResolution); + int startIndex = gridCellStartIndices[indexC1]; + int endIndex = gridCellEndIndices[indexC1]; + if (startIndex == -1) { + continue; + } + + // 2.1.4 Acces each boid in the cell and compute velocity change + for (int i = startIndex; i < endIndex; ++i) { + int bi1 = particleArrayIndices[i]; + + glm::vec3 diff = pos[bi1] - posOfIndex; + float distance = glm::length(diff); + if (bi1 != boidIndex) { + // Rule 1: boids fly towards their local perceived center of mass, which excludes themselves + if (distance < rule1Distance) { + perceivedCenter += pos[bi1]; + ++rule1Neighbor; + } + + // Rule 2: boids try to stay a distance d away from each other + if (distance < rule2Distance) { + collisionP -= diff; + } + + // Rule 3: boids try to match the speed of surrounding boids + if (distance < rule3Distance) { + perceivedVel += vel1[bi1]; + ++rule3Neighbor; + } + } + } + } + } + } + + + if (rule1Neighbor > 0) { + perceivedCenter /= rule1Neighbor; + dVel += (perceivedCenter - posOfIndex) * rule1Scale; + } + + dVel += collisionP * rule2Scale; + + if (rule3Neighbor > 0) { + perceivedVel /= rule3Neighbor; +#if !USE_RULE3_FROM_CONARD_PARKER + dVel += perceivedVel * rule3Scale; // From instruction +#else // USE_RULE3_FROM_CONARD_PARKER + dVel += (perceivedVel - vel1[boidIndex]) * rule3Scale; // From Conard Parker's, maybe it runs much better than the instructions' now? +#endif // USE_RULE3_FROM_CONARD_PARKER + } + + vel2[boidIndex] = vel1[boidIndex] + dVel; + + // 2.1.5 Clamp the speed + clampFunc(vel2[boidIndex], -maxSpeed, maxSpeed); + + // End implemetation } __global__ void kernUpdateVelNeighborSearchCoherent( @@ -329,7 +663,7 @@ __global__ void kernUpdateVelNeighborSearchCoherent( float inverseCellWidth, float cellWidth, int *gridCellStartIndices, int *gridCellEndIndices, glm::vec3 *pos, glm::vec3 *vel1, glm::vec3 *vel2) { - // TODO-2.3 - This should be very similar to kernUpdateVelNeighborSearchScattered, + // DONE-2.3 - This should be very similar to kernUpdateVelNeighborSearchScattered, // except with one less level of indirection. // This should expect gridCellStartIndices and gridCellEndIndices to refer // directly to pos and vel1. @@ -341,18 +675,142 @@ __global__ void kernUpdateVelNeighborSearchCoherent( // - Access each boid in the cell and compute velocity change from // the boids rules, if this boid is within the neighborhood distance. // - Clamp the speed change before putting the new speed in vel2 + + // Start implementation + int index = (blockIdx.x * blockDim.x) + threadIdx.x; + if (index >= N) { + return; + } + glm::vec3 posOfIndex = pos[index]; + // 2.3.1 Identify the cell + glm::vec3 posLocal = posOfIndex - gridMin; + glm::vec3 idxLocal = posLocal * inverseCellWidth; + //int x = idxLocal.x, y = idxLocal.y, z = idxLocal.z; + //int indexCell = gridIndex3Dto1D(x, y, z, gridResolution); + + // 2.3.2 Identify which cells may contain neighbors. +#if !GRID_LOOPING_OPTIMIZATION +#if !USE_HALF_SIZE_OF_CELL + int minX = idxLocal.x - 0.5f, minY = idxLocal.y - 0.5f, minZ = idxLocal.z - 0.5f; + int maxX = idxLocal.x + 0.5f, maxY = idxLocal.y + 0.5f, maxZ = idxLocal.z + 0.5f; +#else // USE_HALF_SIZE_OF_CELL + int minX = idxLocal.x - 1, minY = idxLocal.y - 1, minZ = idxLocal.z - 1; + int maxX = idxLocal.x + 1, maxY = idxLocal.y + 1, maxZ = idxLocal.z + 1; +#endif // USE_HALF_SIZE_OF_CELL +#else // GRID_LOOPING_OPTIMIZATION + float searchRadius = imax(rule1Distance, imax(rule2Distance, rule3Distance)); + float searchDiffIdx = searchRadius * inverseCellWidth; + int minX = idxLocal.x - searchDiffIdx, minY = idxLocal.y - searchDiffIdx, minZ = idxLocal.z - searchDiffIdx; + int maxX = idxLocal.x + searchDiffIdx, maxY = idxLocal.y + searchDiffIdx, maxZ = idxLocal.z + searchDiffIdx; +#endif // GRID_LOOPING_OPTIMIZATION + + minX = imax(minX, 0); + maxX = imin(maxX, gridResolution - 1); + minY = imax(minY, 0); + maxY = imin(maxY, gridResolution - 1); + minZ = imax(minZ, 0); + maxZ = imin(maxZ, gridResolution - 1); + + // 2.3.3 For each cell, ... + // Pay attention to the access order of cells for memory benefits + glm::vec3 dVel(0.f, 0.f, 0.f); + + glm::vec3 perceivedCenter(0.f, 0.f, 0.f); + glm::vec3 collisionP(0.f, 0.f, 0.f); + glm::vec3 perceivedVel(0.f, 0.f, 0.f); + + int rule1Neighbor = 0, rule3Neighbor = 0; + +#if !FOR_LOOP_XYZ + for (int z1 = minZ; z1 <= maxZ; ++z1) { + for (int y1 = minY; y1 <= maxY; ++y1) { + for (int x1 = minX; x1 <= maxX; ++x1) { +#else // FOR_LOOP_XYZ + for (int x1 = minX; x1 <= maxX; ++x1) { + for (int y1 = minY; y1 <= maxY; ++y1) { + for (int z1 = minZ; z1 <= maxZ; ++z1) { +#endif // FOR_LOOP_XYZ + int indexC1 = gridIndex3Dto1D(x1, y1, z1, gridResolution); + int startIndex = gridCellStartIndices[indexC1]; + int endIndex = gridCellEndIndices[indexC1]; + if (startIndex == -1) { + continue; + } + + // 2.3.4 Acces each boid in the cell and compute velocity change + for (int i = startIndex; i < endIndex; ++i) { + glm::vec3 diff = pos[i] - posOfIndex; + float distance = glm::length(diff); + if (i != index) { + // Rule 1: boids fly towards their local perceived center of mass, which excludes themselves + if (distance < rule1Distance) { + perceivedCenter += pos[i]; + ++rule1Neighbor; + } + + // Rule 2: boids try to stay a distance d away from each other + if (distance < rule2Distance) { + collisionP -= diff; + } + + // Rule 3: boids try to match the speed of surrounding boids + if (distance < rule3Distance) { + perceivedVel += vel1[i]; + ++rule3Neighbor; + } + } + } + } + } + } + + + if (rule1Neighbor > 0) { + perceivedCenter /= rule1Neighbor; + dVel += (perceivedCenter - posOfIndex) * rule1Scale; + } + + dVel += collisionP * rule2Scale; + + if (rule3Neighbor > 0) { + perceivedVel /= rule3Neighbor; +#if !USE_RULE3_FROM_CONARD_PARKER + dVel += perceivedVel * rule3Scale; // From instruction +#else // USE_RULE3_FROM_CONARD_PARKER + dVel += (perceivedVel - vel1[index]) * rule3Scale; // From Conard Parker's, maybe it runs much better than the instructions' now? +#endif // USE_RULE3_FROM_CONARD_PARKER + } + + vel2[index] = vel1[index] + dVel; + + // 2.3.5 Clamp the speed + clampFunc(vel2[index], -maxSpeed, maxSpeed); + + // End implemetation } /** * Step the entire N-body simulation by `dt` seconds. */ void Boids::stepSimulationNaive(float dt) { - // TODO-1.2 - use the kernels you wrote to step the simulation forward in time. - // TODO-1.2 ping-pong the velocity buffers + // DONE-1.2 - use the kernels you wrote to step the simulation forward in time. + // DONE-1.2 ping-pong the velocity buffers + + // Start implementation + // 1.2.1 + dim3 fullBlocksPerGrid((numObjects + blockSize - 1) / blockSize); + callCUDA_Profile(kernUpdateVelocityBruteForce)<<>>(numObjects, dev_pos, dev_vel1, dev_vel2); + callCUDA_Profile(kernUpdatePos)<<>>(numObjects, dt, dev_pos, dev_vel2); + + // 1.2.2 + glm::vec3* tmp = dev_vel1; dev_vel1 = dev_vel2; dev_vel2 = tmp;//callCUDA_Profile(cudaMemcpy)(dev_vel1, dev_vel2, numObjects * sizeof(glm::vec3), cudaMemcpyDeviceToDevice); + callCUDA_ProfileEnd(); + + // End implementation } void Boids::stepSimulationScatteredGrid(float dt) { - // TODO-2.1 + // DONE-2.1 // Uniform Grid Neighbor search using Thrust sort. // In Parallel: // - label each particle with its array index as well as its grid index. @@ -364,10 +822,65 @@ void Boids::stepSimulationScatteredGrid(float dt) { // - Perform velocity updates using neighbor search // - Update positions // - Ping-pong buffers as needed + + // Start implementation + dim3 fullBlocksPerGridForBoid((numObjects + blockSize - 1) / blockSize); + + dim3 fullBlocksPerGridForCell((gridCellCount + blockSize - 1) / blockSize); + + //int fullBlocksPerDimForCell = (gridSideCount + blockSizePerDim - 1) / blockSizePerDim; + //dim3 threadsPerBlock3DForCell(blockSizePerDim, blockSizePerDim, blockSizePerDim); + //dim3 fullBlocksPerGrid3DForCell(fullBlocksPerDimForCell, fullBlocksPerDimForCell, fullBlocksPerDimForCell); + + // 2.1.1 Label each particle + callCUDA_Profile(kernComputeIndices)<<>>(numObjects, gridSideCount, gridMinimum, gridInverseCellWidth, dev_pos, dev_particleArrayIndices, dev_particleGridIndices); + + // 2.1.2 Key sort (Key = boid index) +#if !USE_STABLE_SORT + callCUDA_Profile(thrust::sort_by_key)(dev_thrust_particleGridIndices, dev_thrust_particleGridIndices + numObjects, dev_thrust_particleArrayIndices); // Unstable sort +#else // USE_STABLE_SORT + callCUDA_Profile(thrust::stable_sort_by_key)(dev_thrust_particleGridIndices, dev_thrust_particleGridIndices + numObjects, dev_thrust_particleArrayIndices); // Stable sort, for comparison +#endif // USE_STABLE_SORT + + // 2.1.3 Find start and end + callCUDA_Profile(kernResetIntBuffer)<<>>(gridCellCount, dev_gridCellStartIndices, -1); + callCUDA_Profile(kernResetIntBuffer)<<>>(gridCellCount, dev_gridCellEndIndices, gridCellCount); + +#if !IDENTIFY_START_END_BY_BINARY_SEARCH + callCUDA_Profile(kernIdentifyCellStartEnd)<<>>(numObjects, dev_particleGridIndices, dev_gridCellStartIndices, dev_gridCellEndIndices); +#else // IDENTIFY_START_END_BY_BINARY_SEARCH + callCUDA_Profile(kernIdentifyCellStartEnd)<<>>(numObjects, dev_particleGridIndices, dev_gridCellStartIndices, dev_gridCellEndIndices); +#endif // IDENTIFY_START_END_BY_BINARY_SEARCH + + + // 2.1.4 Update velocity using neighbor search + callCUDA_Profile(kernUpdateVelNeighborSearchScattered)<<>>( + numObjects, gridSideCount, gridMinimum, gridInverseCellWidth, gridCellWidth, + dev_gridCellStartIndices, dev_gridCellEndIndices, dev_particleArrayIndices, dev_pos, dev_vel1, dev_vel2); + + // 2.1.5 Update positions + callCUDA_Profile(kernUpdatePos)<<>>(numObjects, dt, dev_pos, dev_vel2); + + // 2.1.6 Ping-pong buffers + glm::vec3* tmp = dev_vel1; dev_vel1 = dev_vel2; dev_vel2 = tmp;//callCUDA_Profile(cudaMemcpy)(dev_vel1, dev_vel2, numObjects * sizeof(glm::vec3), cudaMemcpyDeviceToDevice); + callCUDA_ProfileEnd(); + // End implementation +} + +/** +* Reshuffle `src` to `dst`, using `key`. +*/ +__global__ void kernReshuffle(int N, glm::vec3* dst, const glm::vec3* src, const int* key) { + int index = (blockIdx.x * blockDim.x) + threadIdx.x; + if (index >= N) { + return; + } + int dstIdx = key[index]; + dst[index] = src[dstIdx]; } void Boids::stepSimulationCoherentGrid(float dt) { - // TODO-2.3 - start by copying Boids::stepSimulationNaiveGrid + // DONE-2.3 - start by copying Boids::stepSimulationNaiveGrid // Uniform Grid Neighbor search using Thrust sort on cell-coherent data. // In Parallel: // - Label each particle with its array index as well as its grid index. @@ -382,6 +895,49 @@ void Boids::stepSimulationCoherentGrid(float dt) { // - Perform velocity updates using neighbor search // - Update positions // - Ping-pong buffers as needed. THIS MAY BE DIFFERENT FROM BEFORE. + + // Start implementation + dim3 fullBlocksPerGridForBoid((numObjects + blockSize - 1) / blockSize); + + dim3 fullBlocksPerGridForCell((gridCellCount + blockSize - 1) / blockSize); + + // 2.3.1 Label each particle + callCUDA_Profile(kernComputeIndices)<<>>(numObjects, gridSideCount, gridMinimum, gridInverseCellWidth, dev_pos, dev_particleArrayIndices, dev_particleGridIndices); + + // 2.3.2 Key sort (Key = boid index) +#if !USE_STABLE_SORT + callCUDA_Profile(thrust::sort_by_key)(dev_thrust_particleGridIndices, dev_thrust_particleGridIndices + numObjects, dev_thrust_particleArrayIndices); // Unstable sort +#else // USE_STABLE_SORT + callCUDA_Profile(thrust::stable_sort_by_key)(dev_thrust_particleGridIndices, dev_thrust_particleGridIndices + numObjects, dev_thrust_particleArrayIndices); // Stable sort, for comparison +#endif // USE_STABLE_SORT + + // 2.3.3 Find start and end + callCUDA_Profile(kernResetIntBuffer)<<>>(gridCellCount, dev_gridCellStartIndices, -1); + callCUDA_Profile(kernResetIntBuffer)<<>>(gridCellCount, dev_gridCellEndIndices, gridCellCount); + +#if !IDENTIFY_START_END_BY_BINARY_SEARCH + callCUDA_Profile(kernIdentifyCellStartEnd)<<>>(numObjects, dev_particleGridIndices, dev_gridCellStartIndices, dev_gridCellEndIndices); +#else // IDENTIFY_START_END_BY_BINARY_SEARCH + callCUDA_Profile(kernIdentifyCellStartEnd)<<>>(numObjects, dev_particleGridIndices, dev_gridCellStartIndices, dev_gridCellEndIndices); +#endif // IDENTIFY_START_END_BY_BINARY_SEARCH + + // 2.3.4 Reshuffle pos+vel + callCUDA_Profile(kernReshuffle)<<>>(numObjects, dev_pos_reshuffle, dev_pos, dev_particleArrayIndices); + callCUDA_Profile(kernReshuffle)<<>>(numObjects, dev_vel_reshuffle, dev_vel1, dev_particleArrayIndices); + + // 2.3.5 Update velocity using neighbor search (vel2 is reshuffled) + callCUDA_Profile(kernUpdateVelNeighborSearchCoherent)<<>>( + numObjects, gridSideCount, gridMinimum, gridInverseCellWidth, gridCellWidth, + dev_gridCellStartIndices, dev_gridCellEndIndices, dev_pos_reshuffle, dev_vel_reshuffle, dev_vel2); + + // 2.3.6 Update positions + callCUDA_Profile(kernUpdatePos)<<>>(numObjects, dt, dev_pos_reshuffle, dev_vel2); + + // 2.3.7 Ping-pong buffers + glm::vec3* tmp = dev_vel1; dev_vel1 = dev_vel2; dev_vel2 = tmp;//callCUDA_Profile(cudaMemcpy)(dev_vel1, dev_vel2, numObjects * sizeof(glm::vec3), cudaMemcpyDeviceToDevice); + tmp = dev_pos; dev_pos = dev_pos_reshuffle; dev_pos_reshuffle = tmp;//callCUDA_Profile(cudaMemcpy)(dev_pos, dev_pos_reshuffle, numObjects * sizeof(glm::vec3), cudaMemcpyDeviceToDevice); + callCUDA_ProfileEnd(); + // End implementation } void Boids::endSimulation() { @@ -389,12 +945,33 @@ void Boids::endSimulation() { cudaFree(dev_vel2); cudaFree(dev_pos); - // TODO-2.1 TODO-2.3 - Free any additional buffers here. + // DONE-2.1 DONE-2.3 - Free any additional buffers here. + + // Start implementation 2.3 + cudaFree(dev_pos_reshuffle); + cudaFree(dev_vel_reshuffle); + // End implementation 2.3 + + // Start implementation 2.1 + cudaFree(dev_gridCellEndIndices); + cudaFree(dev_gridCellStartIndices); + cudaFree(dev_particleGridIndices); + cudaFree(dev_particleArrayIndices); + // End implementation 2.1 } void Boids::unitTest() { // LOOK-1.2 Feel free to write additional tests here. + int blockForBoid = (numObjects + blockSize - 1) / blockSize; + int blockForCell = (gridCellCount + blockSizePerDim - 1) / blockSizePerDim; + + std::cout << "numObjects = " << numObjects; + std::cout << "\ngridSize: " << gridSideCount << "^3 = " << gridCellCount; + std::cout << "\nblockForBoid = " << blockForBoid << ", totalThreadForBoid = " << blockForBoid * blockSize; + std::cout << "\nblockForCell = " << blockForCell << ", totalThreadForCell = " << blockForCell * blockSizePerDim; + std::cout << std::endl; + // test unstable sort int *dev_intKeys; int *dev_intValues; diff --git a/src/kernel.h b/src/kernel.h index 3d3da72..5e5e7a6 100644 --- a/src/kernel.h +++ b/src/kernel.h @@ -9,6 +9,46 @@ #include #include +/******************************************** + * Start Performance Analysis Configuration * + ********************************************/ + +#define PFM_ANA_VISUALIZE 1 +#define PFM_ANA_UNIFORM_GRID 1 +#define PFM_ANA_COHERENT_GRID 1 + +#define PFM_ANA_NUM_OBJECTS 5000 +//1000000 //500000 //300000 //150000 +//100000 //60000 //20000 //5000 //1000 + +#define PFM_ANA_blockSize 128 //32 //64 //1024 //512 //256 //128 + +// If true, use rule3 from Conard Parker's note, otherwise follow the instruction. +#define USE_RULE3_FROM_CONARD_PARKER 0 + +// If true, use stable sort, otherwise unstable sort. +#define USE_STABLE_SORT 0 //1 + +// If true, use half cell width and check 27 cells, otherwise check 8 cells. +#define USE_HALF_SIZE_OF_CELL 1 //1 + +// If true, for loop x->y->z, otherwise z->y->x. +#define FOR_LOOP_XYZ 0 //1 + +// If true, identify start and end by binary search (parallel by grid cells), otherwise in constant time (parallel by boids). +#define IDENTIFY_START_END_BY_BINARY_SEARCH 0 //0 + +// If true, use shared memory optimization +#define USE_SHARED_MEMORY 0 + +// If true, adjust the search area by cell width. +#define GRID_LOOPING_OPTIMIZATION 1 +#define GRID_LOOPING_WIDTH 5.0f //5.0f //4.0f //3.0f //2.5f //6.0f //8.0f //10.0f + +/****************************************** + * End Performance Analysis Configuration * + ******************************************/ + namespace Boids { void initSimulation(int N); void stepSimulationNaive(float dt); diff --git a/src/main.cpp b/src/main.cpp index b82c8c6..029a1df 100644 --- a/src/main.cpp +++ b/src/main.cpp @@ -13,12 +13,19 @@ // ================ // LOOK-2.1 LOOK-2.3 - toggles for UNIFORM_GRID and COHERENT_GRID -#define VISUALIZE 1 -#define UNIFORM_GRID 0 -#define COHERENT_GRID 0 +//#define VISUALIZE 0 +//#define UNIFORM_GRID 1 +//#define COHERENT_GRID 1 + +#define VISUALIZE PFM_ANA_VISUALIZE +#define UNIFORM_GRID PFM_ANA_UNIFORM_GRID +#define COHERENT_GRID PFM_ANA_COHERENT_GRID // LOOK-1.2 - change this to adjust particle count in the simulation -const int N_FOR_VIS = 5000; +const int N_FOR_VIS = PFM_ANA_NUM_OBJECTS; +//const int N_FOR_VIS = 100000; +////500000; //300000; //150000; +////100000; //60000; //20000; //5000; const float DT = 0.2f; /** @@ -231,6 +238,9 @@ void initShaders(GLuint * program) { timebase = time; frame = 0; } +#if ENABLE_PROFILE_LOG + utilityCore::ProfileLog::get().step(fps, time * 1000.); +#endif ENABLE_PROFILE_LOG runCUDA(); diff --git a/src/utilityCore.cpp b/src/utilityCore.cpp index 70f4eea..a769b7d 100644 --- a/src/utilityCore.cpp +++ b/src/utilityCore.cpp @@ -160,3 +160,248 @@ void utilityCore::printVec4(const glm::vec4 &m) { void utilityCore::printVec3(const glm::vec3 &m) { std::cout << m[0] << " " << m[1] << " " << m[2] << std::endl; } + +#if ENABLE_PROFILE_LOG + +void utilityCore::ProfileLog::cacheInStep(float avgFPS, float timeElapsed) { + //m_frameRates.push_back(avgFPS); + m_frameRates.push_back(1000.f / timeElapsed); + m_frameDurations.push_back(timeElapsed); + for (size_t i = 0; i < m_cudaEvents.size(); ++i) { + cudaEventSynchronize(m_cudaEvents[i]); + } + m_eventDurations.emplace_back(); + std::unordered_map& eventDurationMap = m_eventDurations.back(); + eventDurationMap.reserve(m_cudaEvents.size() - 1); + for (size_t i = 0; i + 1 < m_cudaEvents.size(); ++i) { + eventDurationMap[i] = 0.f; + cudaEventElapsedTime(&eventDurationMap[i], m_cudaEvents[i], m_cudaEvents[i + 1]); + } +} + +utilityCore::ProfileLog& utilityCore::ProfileLog::get() { + static ProfileLog profileLog; + return profileLog; +} + +bool utilityCore::ProfileLog::isRecordingEvent() const { + return m_recording; +} + +bool utilityCore::ProfileLog::isProfiling() const { + return m_profiling; +} + +const std::string& utilityCore::ProfileLog::getEndEventName() const { + return m_endEventName; +} + +void utilityCore::ProfileLog::clearArgs() { + m_args.empty(); + m_kwargs.empty(); +} + +void utilityCore::ProfileLog::addArg(const std::string& arg) { + m_args.push_back(arg); +} + +void utilityCore::ProfileLog::addKwArg(const std::string& key, int value) { + if (m_kwargs.find(key) == m_kwargs.end()) { + m_kwargs_keys.push_back(key); + } + m_kwargs[key] = value; +} + +void utilityCore::ProfileLog::initProfile(const std::string& prefix, const std::string& endEventName, int startFrame, int frameCount, int frameInterval, std::ios_base::openmode mode) { + m_mode = mode; + m_endEventName = endEventName; + m_startFrame = startFrame; + m_endFrame = startFrame + frameCount * frameInterval; + + //m_startTime = startTime; + m_frameInterval = frameInterval; + + //m_dataSizeToCount = (m_endFrame - m_startFrame) / m_frameInterval; + m_dataSizeToCount = frameCount; + + m_frameRates.reserve(m_dataSizeToCount); + m_frameDurations.reserve(m_dataSizeToCount); + m_eventDurations.reserve(m_dataSizeToCount); + + m_filePath = prefix; + //for (const std::pair& kw : m_kwargs) { + // m_filePath.append("," + kw.first + "_" + std::to_string(kw.second)); + for(const std::string& arg : m_kwargs_keys) { + m_filePath.append("," + arg + "_" + std::to_string(m_kwargs[arg])); + } + for (const std::string& arg : m_args) { + m_filePath.append("," + arg); + } + m_filePath.append(".csv"); + m_recording = true; +} + +size_t utilityCore::ProfileLog::registerEvent(const std::string& eventName) { + auto it = m_cudaEventIndices.find(eventName); + if (it != m_cudaEventIndices.end()) { + return it->second; + } + size_t result = m_cudaEvents.size(); + m_cudaEventNames.push_back(eventName); + m_cudaEventIndices[eventName] = result; + m_cudaEvents.emplace_back(); + cudaEvent_t& newEvent = m_cudaEvents.back(); + cudaEventCreate(&newEvent); + return result; +} + +void utilityCore::ProfileLog::recordEvent(const std::string& eventName) { + size_t index = registerEvent(eventName); + if (!m_recording) { + return; + } + cudaEvent_t& event = m_cudaEvents[index]; + cudaEventRecord(event); +} + +void utilityCore::ProfileLog::step(float avgFPS, float timeStamp){ + float timeElapsed = timeStamp - m_timeStamp; + m_timeStamp = timeStamp; + m_profiling = false; + + if (m_frameCount >= m_startFrame && m_frameCount < m_endFrame) { + //if(timeStamp >= m_startTime) { + // if (m_startFrame == -1) { + // m_startFrame = m_frameCount; + // m_endFrame = m_frameCount + m_dataSizeToCount * m_frameInterval; + // } + // if (m_frameCount < m_endFrame) { + if (m_frameInterval <= 1 || (m_frameCount - m_startFrame) % m_frameInterval == 0) { + cacheInStep(avgFPS, timeElapsed); + } + m_profiling = true; + // } + } + + if(!m_profiling) { + if (m_recording && m_frameCount >= m_endFrame) { + writeToFile(m_mode); + unregisterEvents(); + } + } + ++m_frameCount; +} +void utilityCore::ProfileLog::writeToFile(std::ios_base::openmode mode) { + if (m_eventDurations.size() == 0) { + return; + } + + m_fout.open(m_filePath, mode); + m_fout << ",fps,duration"; + m_fout << ",cudaTotal"; + for (size_t i = 0; i + 1 < m_cudaEventNames.size(); ++i) { + const std::string& header = m_cudaEventNames[i]; + m_fout << ',' << header; + } + m_fout << std::endl; + int frameCount = m_startFrame; + + double avgFPS = 0., avgDuration = 0., avgTotal = 0.; + double maxFPS = 0., maxDuration = 0., maxTotal = 0.; + double minFPS = std::numeric_limits::max(), minDuration = std::numeric_limits::max(), minTotal = std::numeric_limits::max(); + + std::unordered_map avgEventDurations; + std::unordered_map maxEventDurations; + std::unordered_map minEventDurations; + + for (std::pair& eventPair : m_eventDurations[0]) { + avgEventDurations[eventPair.first] = 0.; + maxEventDurations[eventPair.first] = 0.; + minEventDurations[eventPair.first] = std::numeric_limits::max(); + } + double invCount = 1. / m_eventDurations.size(); + + std::stringstream ss; + + for (size_t i = 0; i < m_frameRates.size(); ++i) { + double cudaTotalDuration = 0.; + + avgFPS += m_frameRates[i] * invCount; + avgDuration += m_frameDurations[i] * invCount; + + maxFPS = std::max(maxFPS, m_frameRates[i]); + minFPS = std::min(minFPS, m_frameRates[i]); + + maxDuration = std::max(maxDuration, m_frameDurations[i]); + minDuration = std::min(minDuration, m_frameDurations[i]); + + //m_fout << frameCount << ',' << m_frameRates[i] << ',' << m_frameDurations[i]; + + std::unordered_map& eventDurationMap = m_eventDurations[i]; + for (std::pair& eventPair : eventDurationMap) { + avgEventDurations[eventPair.first] += eventPair.second * invCount; + maxEventDurations[eventPair.first] = std::max(maxEventDurations[eventPair.first], eventPair.second); + minEventDurations[eventPair.first] = std::min(minEventDurations[eventPair.first], eventPair.second); + cudaTotalDuration += eventPair.second; + //m_fout << ',' << eventPair.second; + } + avgTotal += cudaTotalDuration * invCount; + maxTotal = std::max(maxTotal, static_cast(cudaTotalDuration)); + minTotal = std::min(minTotal, static_cast(cudaTotalDuration)); + //m_fout << ',' << cudaTotalDuration << std::endl; + } + m_fout << "avg_" << m_frameRates.size() << ',' << avgFPS << ',' << avgDuration; + m_fout << ',' << avgTotal; + for (std::pair& eventPair : avgEventDurations) { + m_fout << ',' << eventPair.second; + } + m_fout << std::endl; + + m_fout << "max_" << m_frameRates.size() << ',' << maxFPS << ',' << maxDuration; + m_fout << ',' << maxTotal; + for (std::pair& eventPair : maxEventDurations) { + m_fout << ',' << eventPair.second; + } + m_fout << std::endl; + + m_fout << "min_" << m_frameRates.size() << ',' << minFPS << ',' << minDuration; + m_fout << ',' << minTotal; + for (std::pair& eventPair : minEventDurations) { + m_fout << ',' << eventPair.second; + } + m_fout << std::endl; + + for (size_t i = 0; i < m_frameRates.size(); ++i) { + double cudaTotalDuration = 0.; + + m_fout << frameCount << ',' << m_frameRates[i] << ',' << m_frameDurations[i]; + + std::unordered_map& eventDurationMap = m_eventDurations[i]; + for (std::pair& eventPair : eventDurationMap) { + avgEventDurations[eventPair.first] += eventPair.second * invCount; + cudaTotalDuration += eventPair.second; + //m_fout << ',' << eventPair.second; + } + m_fout << ',' << cudaTotalDuration; + for (std::pair& eventPair : eventDurationMap) { + m_fout << ',' << eventPair.second; + } + m_fout << std::endl; + frameCount += m_frameInterval; + } + + m_fout.close(); + + std::cout << "Write profile at " << m_filePath << std::endl; +} + +void utilityCore::ProfileLog::unregisterEvents() { + m_recording = false; + m_cudaEventIndices.empty(); + m_cudaEventNames.empty(); + for (size_t i = 0; i < m_cudaEvents.size(); ++i) { + cudaEventDestroy(m_cudaEvents[i]); + } + m_cudaEvents.empty(); +} +#endif // ENABLE_PROFILE_LOG diff --git a/src/utilityCore.hpp b/src/utilityCore.hpp index bf7c05f..503e9a5 100644 --- a/src/utilityCore.hpp +++ b/src/utilityCore.hpp @@ -46,3 +46,87 @@ extern void printMat4(const glm::mat4 &); extern void printVec4(const glm::vec4 &); extern void printVec3(const glm::vec3 &); } + + +/********************* +* Start Profile Log * +*********************/ + +#define ENABLE_PROFILE_LOG 0//0 + +#if ENABLE_PROFILE_LOG +#include +#include +namespace utilityCore { +class ProfileLog { +private: + std::ofstream m_fout; + std::string m_filePath; + std::string m_endEventName; + + bool m_recording = false; + bool m_profiling = false; + + std::vector m_args; + std::vector m_kwargs_keys; + std::unordered_map m_kwargs; + + std::ios_base::openmode m_mode = std::ios_base::out; + float m_timeStamp = 0.f; + int m_frameCount = 0, m_dataSizeToCount = 2048; + int m_startFrame = -1, m_endFrame = -1; + float m_startTime = 2000.f; + int m_frameInterval = 1; + + std::vector m_frameRates; + std::vector m_frameDurations; + std::vector> m_eventDurations; + + std::unordered_map m_cudaEventIndices; + std::vector m_cudaEventNames; + std::vector m_cudaEvents; + +private: + void cacheInStep(float avgFPS, float timeElapsed); + +public: + static ProfileLog& get(); + bool isRecordingEvent() const; + bool isProfiling() const; + const std::string& getEndEventName() const; + + void clearArgs(); + void addArg(const std::string& arg); + void addKwArg(const std::string& key, int value); + + void initProfile(const std::string& prefix, const std::string& endEventName = "end", int startFrame = 256, int frameCount = 2048, int frameInterval = 1, std::ios_base::openmode mode = std::ios_base::out); + size_t registerEvent(const std::string& eventName); + void recordEvent(const std::string& eventName); + + // Call before runCUDA, timeStamp in ms + void step(float avgFPS, float timeStamp); + + void writeToFile(std::ios_base::openmode mode = std::ios_base::out); + void unregisterEvents(); +}; +} + +#define callCUDA_Profile(funcName) \ + utilityCore::ProfileLog::get().recordEvent(#funcName "-" + std::to_string(__LINE__)); \ + funcName + +#define callCUDA_ProfileWithAlias(alias, funcName) \ + utilityCore::ProfileLog::get().recordEvent(#alias); \ + funcName + +#define callCUDA_ProfileEnd() utilityCore::ProfileLog::get().recordEvent(utilityCore::ProfileLog::get().getEndEventName()) + +#else // ENABLE_PROFILE_LOG +#define callCUDA_Profile(funcName) funcName +#define callCUDA_ProfileWithAlias(alias, funcName) funcName +#define callCUDA_ProfileEnd() +#endif // ENABLE_PROFILE_LOG + +/******************* +* End Profile Log * +*******************/