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a4166e3d8ff4e35932510aaff7aa90772f84b4d07e9f6f83c614cba7ceefe0eb + md5: 6432cb5d4ac0046c3ac0a8a0f95842f9 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=13 + - libstdcxx >=13 + - libzlib >=1.3.1,<2.0a0 + license: BSD-3-Clause + license_family: BSD + purls: [] + size: 567578 + timestamp: 1742433379869 diff --git a/content/slides/5-intro-to-gpu-prog-models-high-level/pixi.toml b/content/slides/5-intro-to-gpu-prog-models-high-level/pixi.toml new file mode 100644 index 0000000..595bb1c --- /dev/null +++ b/content/slides/5-intro-to-gpu-prog-models-high-level/pixi.toml @@ -0,0 +1,18 @@ +[workspace] +authors = ["Ashwin V. Mohanan "] +channels = ["conda-forge"] +name = "5-intro-to-gpu-prog-models-high-level" +platforms = ["linux-64"] +version = "0.1.0" + +[tasks] + +[dependencies] +python = ">=3.13.9,<3.14" +cupy = ">=13.6.0,<14" +jupyterlab = ">=4.1.0,<5" +ipython = ">=9.7.0,<10" +numba-cuda = ">=0.20.1,<0.21" + +[pypi-dependencies] +jupyterlab-rise = ">=0.43.1, <0.44" diff --git a/content/slides/5-intro-to-gpu-prog-models-high-level/talk.html b/content/slides/5-intro-to-gpu-prog-models-high-level/talk.html new file mode 100644 index 0000000..588f96e --- /dev/null +++ b/content/slides/5-intro-to-gpu-prog-models-high-level/talk.html @@ -0,0 +1,672 @@ + + + + + + + + High level language support + + + + + + + + + +
+
+ +
+

High level language support

+

+

Ashwin Mohanan

+

ENCCS/RISE

+

2025-11-20

+
+ +
+

Outline

+

Introduce different libraries, show similarities and differences in +capabilities

+
    +

  • GPU programming with Python

    +

    Numba, Cupy, Jax, PyCUDA/PyOpenCL, CUDA Python / HIP Python

    • +

  • GPU programming with Julia

    +

    CUDA.jl / AMDGPU.jl / oneAPI.jl / Metal.jl and +KernelAbstractions

    • +
+
+
+ +

What is high-level +programming?

+
+

Primer on Python and Numpy

+
+

Either use simple data structures in Python’s standard library and +for-loops

+
import random
+import math
+# Element-wise sum of two "arrays" represented as lists
+a = [random.random() for _ in range(1000)]
+b = [random.random() for _ in range(1000)]
+
+c = []
+for a_i, b_i in zip(a, b):
+    c.append(a_i + math.sin(b_i))
+
+
+ +

What is high-level +programming?

+
+

Primer on Python and Numpy

+
+

… or you use efficient data structures and +vectorized operations

+

with expressive syntax!

+
import numpy as np
+a = np.random.random(size=1000)
+b = np.random.random(size=1000)
+c = a + np.sin(b)
+
+
+ +

Numba

+

+
    +

  • JIT compiler which supports a subset of Python and Numpy

    • +

  • CPU execution

    +

    via LLVM using llvmlite and a C compiler

    • +

  • GPU execution

    +

    via CUDA using nvcc and nvrtc / +cudatoolkit

    • +
+
+
+ +

Numba

+
    +
  • Experimental AMD GPU support through numba-hip +extension
    • +
+
from numba import hip
+# Use `@hip.jit` decorator, or
+# Call `hip.pose_as_cuda()` and use `@cuda.jit`
+
+
+ +

Numba: ufuncs +(element-wise vectorized)

+
import math
+import numpy as np
+import numba
+
+@numba.vectorize(
+    [numba.float64(numba.float64, numba.float64)], 
+    target="cuda"
+)
+def a_plus_sin_b(a, b):
+    return a + math.sin(b)
+
+N = 10_000_000
+vec = np.random.rand(N)
+a_plus_sin_b(vec, vec)
+
+
+ +

Numba: low-level

+

Control execution using numba.cuda.threadIdx, +blockDim, blockIdx, gridDim +etc.

+
import math
+import numpy as np
+import numba
+
+@numba.cuda.jit
+def a_plus_sin_b(a, b, c):
+    """GPU vectorized addition. Computes C = A + B"""
+    # like threadIdx.x + (blockIdx.x * blockDim.x)
+    thread_id = numba.cuda.grid(ndim=1)
+    size = len(c)
+
+    if thread_id < size:
+        c[thread_id] = a[thread_id] + math.sin(b[thread_id])
+
+N = 10_000_000
+a = numba.cuda.to_device(np.random.random(N))
+b = numba.cuda.to_device(np.random.random(N))
+c = numba.cuda.device_array_like(a)
+
+grid_size = len(a)
+a_plus_sin_b.forall(grid_size)(a, b, c)
+c.copy_to_host()
+
+
+ +

Jax

+

+
    +

  • Support a subset of Python and Numpy

    • +

  • Drop-in replacement for Numpy

    +

    import jax.numpy as jnp

    • +

  • Interoperable, uses just-in-time XLA compiler to target CPU, GPU +(CUDA and ROCm) and TPU.

    • +
+
+
+ +

Jax as drop-in replacement +for Numpy

+
import numpy as np
+
+data = np.random.random((10, 10_000))
+data[5, 42] = np.nan
+data[7, 1111] = np.nan
+
+# compute 90th percentile ignoring NaNs, 
+# and along the rows of an array
+np.nanpercentile(data, 90, axis=0)
+
+import jax.numpy as jnp
+jnp.nanpercentile(data, 90, axis=0)
+
+
+ +

Jax as JIT compiler

+
import numpy as np
+import jax.numpy as jnp
+from jax import jit
+
+def a_plus_sin_b_numpy(x, y):
+    return x + np.sin(y)
+
+@jit
+def a_plus_sin_b_jax(x, y):
+    return x + jnp.sin(y)
+
+N = 10_000_000
+vec = np.random.rand(N)
+a_plus_sin_b_numpy(vec, vec)
+a_plus_sin_b_jax(vec, vec)  # slightly faster on CPU
+
+import jax
+print(jax.devices())  # check for CUDA
+vec_d = jax.device_put(mx)
+
+a_plus_sin_b_jax(vec_d, vec_d)  # much faster on GPU
+
+
+ +

CuPy

+

+
    +
  • Supports a subset of Python, Numpy and Scipy
    • +
  • Precompiled wheels for CUDA and ROCm
    • +
  • Drop-in support for Numpy and Scipy +python import cupy as cp import cupyx.scipy.fft as cufft
    • +
  • JIT compilation
    • +
  • Array +API support => Cupy arrays can use Numpy functions
    • +
+
+
+ +

CuPy as drop-in +replacement for Numpy

+
import cupy as cp
+a = cp.random.random(size=1000)
+b = cp.random.random(size=1000)
+c = a + cp.sin(b)
+
+
+ +

CuPy as compiler for custom +kernels

+
a_plus_sin_b = cp.ElementwiseKernel(
+   'float64 a, float64 b',
+   'float64 c',
+   'c = a + sin(b)',
+   'a_plus_sin_b'
+)
+a_plus_sin_b
+
+
+ +

Libraries for lower +level implementation

+
    +
  • CUDA Python
    • +
  • HIP Python
    • +
+

HIP Python + hip-python-as-cuda makes it interoperable +with CUDA Python code

+

Third-party implementations

+
    +
  • PyCUDA
    • +
  • PyOpenCL
    • +
+
+
+ +

Summary of Python libraries

+
+ ++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
FeatureNumbaJaxCuPyCuda / HIP PythonPyCUDA / PyOpenCL
Low levelβœ…πŸ€·βœ…βœ…βœ…
High levelβœ…βœ…βœ…βŒβŒ
Numpy compatβœ…βœ…βœ…πŸ€·πŸ€·
CUDA/ROCm interoperabilityπŸ€·βœ…βœ…βœ…πŸ€·
Pre-compiled kernelβŒβœ…βœ…βŒβŒ
Custom kernelsβœ…βœ…βœ…βœ…βœ…
+
+
+
+ +
+

Julia

+

+
    +
  • Base arrays
    • +
  • JuliaGPU project
    • +
  • CUDA.jl, AMDGPU.jl, oneAPI.jl, Metal.jl
    • +
  • KernelAbstractions
    • +
+
+
+
+ +

About Julia

+
+

Primer on Julia and the Base libary

+
+
    +
  • First, released in 2012 and inspired by MATLAB, Lua, Lisp, Python +….
    • +
  • JIT compiled with its core implemented using C and LLVM
    • +
+
+
+ +

Array programming in Julia

+
+

Primer on Julia and the Base libary

+
+
a = rand(1000);
+b = rand(1000);
+
+c = a .+ sin.(b)
+
    +
  • Batteries included with AbstractArray and +Base.Array
    • +
  • Sub-types of arrays: Vector (1D), Matrix +(2D), Array (N-D)
    • +
+
+
+ +

CUDA.jl, AMDGPU.jl, +oneAPI.jl, Metal.jl

+
using CUDA  # or AMDGPU or oneAPI or Metal
+
+a = rand(1000);
+a_d = CuArray(a);  # or ROCArray, oneArray, MtlArray
+
+b_d = CUDA.rand(1000)
+
+# Same builtin function from Base can be used
+c = a_d .+ sin.(b_d)
+
+
+ +
+

+KernelAbstractions for generic functions +

+
using KernelAbstractions
+
+@kernel function a_plus_sin_b!(C, @Const(A), @Const(B))
+     i = @index(Global)  # get threadIdx
+     @inbounds C[i] = A[i] + sin(B[i])
+end
+
using CUDA
+a_d = CUDA.rand(1000)
+b_d = CUDA.rand(1000)
+
+c_d = similar(a_d)
+
+backend = get_backend(a_d)
+a_plus_sin_b!(backend, size(c_d))(
+    c_d, a_d, b_d, ndrange=size(c_d)
+)
+
+
+
+
+ + + + + + + + + + + diff --git a/content/slides/5-intro-to-gpu-prog-models-high-level/talk.md b/content/slides/5-intro-to-gpu-prog-models-high-level/talk.md new file mode 100644 index 0000000..d0683be --- /dev/null +++ b/content/slides/5-intro-to-gpu-prog-models-high-level/talk.md @@ -0,0 +1,364 @@ +--- +title: High level language support +subtitle: '' +author: Ashwin Mohanan +institute: ENCCS/RISE +date: 2025-11-20 +theme: league +transition: fade +highlightTheme: monokai # Does not work with pandoc +--- + +# Outline + +Introduce different libraries, show similarities and differences in capabilities + +- GPU programming with Python + + Numba, Cupy, Jax, PyCUDA/PyOpenCL, CUDA Python / HIP Python + +- GPU programming with Julia + + CUDA.jl / AMDGPU.jl / oneAPI.jl / Metal.jl and KernelAbstractions + + +--- + +## What is high-level programming? + +> Primer on Python and Numpy + +Either use simple data structures in Python's standard library and for-loops + +```python +import random +import math +# Element-wise sum of two "arrays" represented as lists +a = [random.random() for _ in range(1000)] +b = [random.random() for _ in range(1000)] + +c = [] +for a_i, b_i in zip(a, b): + c.append(a_i + math.sin(b_i)) +``` +--- + +## What is high-level programming? + +> Primer on Python and Numpy + +... or you use **efficient data structures** and **vectorized operations** + +with expressive syntax! + +```python +import numpy as np +a = np.random.random(size=1000) +b = np.random.random(size=1000) +c = a + np.sin(b) +``` + + + +--- + +## Numba + + + + +- JIT compiler which supports a subset of Python and Numpy +- **CPU** execution + + via LLVM using `llvmlite` and a C compiler +- **GPU** execution + + via CUDA using `nvcc` and `nvrtc` / `cudatoolkit` + +--- + +## Numba + +- Experimental AMD GPU support through `numba-hip` extension + +```python +from numba import hip +# Use `@hip.jit` decorator, or +# Call `hip.pose_as_cuda()` and use `@cuda.jit` +``` + + +--- + +## Numba: ufuncs (element-wise vectorized) + +```python +import math +import numpy as np +import numba + +@numba.vectorize( + [numba.float64(numba.float64, numba.float64)], + target="cuda" +) +def a_plus_sin_b(a, b): + return a + math.sin(b) + +N = 10_000_000 +vec = np.random.rand(N) +a_plus_sin_b(vec, vec) +``` + +--- + +## Numba: low-level + +Control execution using `numba.cuda.threadIdx`, `blockDim`, `blockIdx`, `gridDim` etc. + +```python +import math +import numpy as np +import numba + +@numba.cuda.jit +def a_plus_sin_b(a, b, c): + """GPU vectorized addition. Computes C = A + B""" + # like threadIdx.x + (blockIdx.x * blockDim.x) + thread_id = numba.cuda.grid(ndim=1) + size = len(c) + + if thread_id < size: + c[thread_id] = a[thread_id] + math.sin(b[thread_id]) + +N = 10_000_000 +a = numba.cuda.to_device(np.random.random(N)) +b = numba.cuda.to_device(np.random.random(N)) +c = numba.cuda.device_array_like(a) + +grid_size = len(a) +a_plus_sin_b.forall(grid_size)(a, b, c) +c.copy_to_host() +``` + +--- + +## Jax + + + + +- Support a subset of Python and Numpy +- Drop-in replacement for Numpy + + `import jax.numpy as jnp` + + - Interoperable, uses just-in-time XLA compiler to target CPU, GPU (CUDA and ROCm) and TPU. + +--- + +## Jax as drop-in replacement for Numpy + +```python +import numpy as np + +data = np.random.random((10, 10_000)) +data[5, 42] = np.nan +data[7, 1111] = np.nan + +# compute 90th percentile ignoring NaNs, +# and along the rows of an array +np.nanpercentile(data, 90, axis=0) + +import jax.numpy as jnp +jnp.nanpercentile(data, 90, axis=0) +``` + +--- + +## Jax as JIT compiler + +```python +import numpy as np +import jax.numpy as jnp +from jax import jit + +def a_plus_sin_b_numpy(x, y): + return x + np.sin(y) + +@jit +def a_plus_sin_b_jax(x, y): + return x + jnp.sin(y) + +N = 10_000_000 +vec = np.random.rand(N) +a_plus_sin_b_numpy(vec, vec) +a_plus_sin_b_jax(vec, vec) # slightly faster on CPU + +import jax +print(jax.devices()) # check for CUDA +vec_d = jax.device_put(mx) + +a_plus_sin_b_jax(vec_d, vec_d) # much faster on GPU +``` + +--- + +## CuPy + + + +- Supports a subset of Python, Numpy and Scipy +- Precompiled wheels for CUDA and ROCm +- Drop-in support for Numpy and Scipy + ```python + import cupy as cp + import cupyx.scipy.fft as cufft + ``` +- JIT compilation +- [Array API](https://data-apis.org/array-api/2023.12/index.html) support => Cupy arrays can use Numpy functions + +--- + +## CuPy as drop-in replacement for Numpy + + +```python +import cupy as cp +a = cp.random.random(size=1000) +b = cp.random.random(size=1000) +c = a + cp.sin(b) +``` + +--- + +## CuPy as compiler for custom kernels + +```python +a_plus_sin_b = cp.ElementwiseKernel( + 'float64 a, float64 b', + 'float64 c', + 'c = a + sin(b)', + 'a_plus_sin_b' +) +a_plus_sin_b +``` + +--- + +## Libraries for lower level implementation + +- CUDA Python +- HIP Python + +HIP Python + `hip-python-as-cuda` makes it interoperable with CUDA Python code + +**Third-party implementations** + +- PyCUDA +- PyOpenCL + +--- + +## Summary of Python libraries + +
+ + +| Feature | Numba | Jax | CuPy | Cuda / HIP Python | PyCUDA / PyOpenCL | +| ------------------- | ----- | --- | ---- | ----------------- | ----------------- | +| Low level | βœ… | 🀷 | βœ… | βœ… | βœ… | +| High level | βœ… | βœ… | βœ… | ❌ | ❌ | +| Numpy compat | βœ… | βœ… | βœ… | 🀷 | 🀷 | +| CUDA/ROCm interoperability | 🀷 | βœ… | βœ… | βœ… | 🀷 | +| Pre-compiled kernel | ❌ | βœ… | βœ… | ❌ | ❌ | +| Custom kernels | βœ… | βœ… | βœ… | βœ… | βœ… | + +
+ +--- + +
+ +## Julia + + + + + +- Base arrays +- JuliaGPU project +- CUDA.jl, AMDGPU.jl, oneAPI.jl, Metal.jl +- KernelAbstractions + +
+ +--- + +## About Julia + +> Primer on Julia and the Base libary + +- First, released in 2012 and inspired by MATLAB, Lua, Lisp, Python .... +- JIT compiled with its core implemented using C and LLVM + + +--- + +## Array programming in Julia + +> Primer on Julia and the Base libary + +```julia +a = rand(1000); +b = rand(1000); + +c = a .+ sin.(b) +``` + +- Batteries included with `AbstractArray` and Base.Array +- Sub-types of arrays: `Vector` (1D), `Matrix` (2D), `Array` (N-D) + +--- + +## CUDA.jl, AMDGPU.jl, oneAPI.jl, Metal.jl + +```julia +using CUDA # or AMDGPU or oneAPI or Metal + +a = rand(1000); +a_d = CuArray(a); # or ROCArray, oneArray, MtlArray + +b_d = CUDA.rand(1000) + +# Same builtin function from Base can be used +c = a_d .+ sin.(b_d) +``` + +--- + +
+

KernelAbstractions for generic functions

+ +```julia +using KernelAbstractions + +@kernel function a_plus_sin_b!(C, @Const(A), @Const(B)) + i = @index(Global) # get threadIdx + @inbounds C[i] = A[i] + sin(B[i]) +end +``` + +```julia +using CUDA +a_d = CUDA.rand(1000) +b_d = CUDA.rand(1000) + +c_d = similar(a_d) + +backend = get_backend(a_d) +a_plus_sin_b!(backend, size(c_d))( + c_d, a_d, b_d, ndrange=size(c_d) +) +``` + +