FLOPS & Latency Benchmarks

This repo tries to answer the question "for a specific input size, and (simple) computation, which framework/accelerator should I choose?".

By implementing the same computation on all available frameworks/accelerators (CPU FPU baseline, SIMD, threads, GPU, FPGA, multi-machine, ...) and running with input sizes from 1 float to 1 billion (or more) floats, we'll see which framework is optimal for which input size.

Frameworks:

• single-threaded scalar/FPU-only
• SIMD
  • SIMD compiler auto-vectorisation
  • SIMD explicit (std::experimental::simd) (equivalent to gcc/clang vector intrinsics)
  • -fopenmp-simd OpenMP SIMD
• threading
  • -fopenmp OpenMP threading
  • C++11 threads
  • std::execution::par_unseq clang parallel stl (pstl)
• GPU
  • -fopenmp -fopenmp-targets=nvptx64 OpenMP GPU offloading
  • GPU compute shaders via vulkan via kompute
  • Halide?
  • Kokkos?
  • SYCL
• multi-machine
  • OpenMP remote offloading
  • manual messaging
  • MPI

TODO:

• flops/byte estimation for the two different calculations
• flops/byte (maximum FLOPS throughput / maximum memory bandwidth) estimation for all the different hardware (CPU, SIMD, threaded, GPU, multi-machine)
• is it possible to create a flops/byte measurement tool, and a set of stress tests to measure

Similar projects

• https://github.com/ashvardanian/ParallelReductionsBenchmark

Parallel Programming Ecosystem Comparison

a parallel programming ecosystem needs:

• language/language-extensions/compiler for describing parallelism, tasks, async, dependencies, etc
• backends for SIMD, multi-core, GPU, and multi-machine
• standard algorithms (blas, sort, reduce, etc)
• tools for debugging and profiling

ecosystem	compiler	SIMD	Multi-core	GPU	Multi-machine	sort	reduce	blas
C++ STL	any (plain c++)	std::ex::simd	std::thread	std::executors (future)	❔ asio? (future)	std::sort par_unseq	std::ex::parallel::reduce	stdblas (future)
OpenMP	gcc, clang, icc (pragma extended c++)	✔️	✔️	✔️	✔️	❌	✔️	❔ OpenBLAS? Eigen?
sandia Kokkos	any (plain c++)	✔️	✔️	✔️	✔️ MPI	✔️	✔️	✔️ stdblas
intel oneAPI	intel dpc++ (sycl)	✔️	✔️	✔️	✔️ MPI	✔️ TBB	✔️ TBB	✔️ MKL
nvidia CUDA	clang, nvc++ (extended c++)	❌	❌	✔️	✔️ NCCL	✔️ thrust / libcu++	✔️ thrust / libcu++	✔️ cutlass / cuBLAS

Dependencies

sudo pacman -S \
    cmake clang \
    benchmark \
    python-matplotlib python-pandas \
    openmp \
    vulkan-tools vulkan-driver vulkan-headers glslang \
    eigen

Running

./test.sh

Example

For O(N) vector addition on specific hardware, this graph answers the question in the following way:

• for input size <= 2^8 floats, use CPU SIMD
• for input size between 2^12 and 2^20 floats, use OpenMP
• for input size >= 2^24 floats (and negligible device/host memory transfer cost) use the GPU

For O(N^3) matrix multiplication