ParallelReductionsBenchmark VS alpaka

Compare ParallelReductionsBenchmark vs alpaka and see what are their differences.

ParallelReductionsBenchmark

Thrust, CUB, TBB, AVX2, CUDA, OpenCL, OpenMP, SyCL - all it takes to sum a lot of numbers fast! (by ashvardanian)
gpu-computing halide Sycl Vulkan Glsl Opencl GPU Cuda thrust Stl Tbb Intel Simd Openmp Gpgpu gpu-acceleration HPC Nvidia Parallel
Source Code
unum.cloud
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alpaka

Abstraction Library for Parallel Kernel Acceleration :llama: (by alpaka-group)
Cuda HPC GPU Rocm Hip Openmp openacc heterogeneous-parallel-programming CPP header-only Tbb Cpp17
Source Code
alpaka.readthedocs.io
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ParallelReductionsBenchmark alpaka
2 1
59 324
- 3.7%
4.6 9.2
5 months ago 4 days ago
C++ C++
- Mozilla Public License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

ParallelReductionsBenchmark

Posts with mentions or reviews of ParallelReductionsBenchmark. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-02-02.
  • Failing to Reach 204 GB/S DDR4 Bandwidth
    3 projects | news.ycombinator.com | 2 Feb 2022
    For the single threaded version, they have a data hazard on the sums that could be smoothed out with a little loop unrolling and separate variables.

    But in the [threaded version](https://github.com/unum-cloud/ParallelReductions/blob/fd16d9...) they have separate slots for an accumulator but it's still in a shared vector, which most likely has the issue I described.

alpaka

Posts with mentions or reviews of alpaka. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-08-01.
  • Cross Platform GPU-Capable Framework?
    6 projects | /r/gpgpu | 1 Aug 2021
    Note that Kokkos uses CUDA, OpenMP and also SYCL in order to have a wide range of targets. I'd also suggest taking a look at Alpaka https://github.com/alpaka-group/alpaka which is similar in some ways.

What are some alternatives?

When comparing ParallelReductionsBenchmark and alpaka you can also consider the following projects:

MatX - An efficient C++17 GPU numerical computing library with Python-like syntax

clspv - Clspv is a compiler for OpenCL C to Vulkan compute shaders

ispc - Intel® Implicit SPMD Program Compiler

GLSL - GLSL Shading Language Issue Tracker

gpuowl - GPU Mersenne primality test.

kompute - General purpose GPU compute framework built on Vulkan to support 1000s of cross vendor graphics cards (AMD, Qualcomm, NVIDIA & friends). Blazing fast, mobile-enabled, asynchronous and optimized for advanced GPU data processing usecases. Backed by the Linux Foundation.

cuda_memtest - Fork of CUDA GPU memtest :eyeglasses:

OpenCLOn12 - The OpenCL-on-D3D12 mapping layer

eaminer - Heterogeneous Ethereum Miner with support for AMD, Intel and Nvidia GPUs using SYCL, OpenCL and CUDA backends

clvk - Implementation of OpenCL 3.0 on Vulkan

relion - Image-processing software for cryo-electron microscopy

ginkgo - Numerical linear algebra software package

ParallelReductionsBenchmark vs MatX alpaka vs clspv ParallelReductionsBenchmark vs ispc alpaka vs GLSL ParallelReductionsBenchmark vs gpuowl alpaka vs kompute ParallelReductionsBenchmark vs cuda_memtest alpaka vs OpenCLOn12 ParallelReductionsBenchmark vs eaminer alpaka vs clvk ParallelReductionsBenchmark vs relion alpaka vs ginkgo