high performance computing on graphics processing units: hgpu.org

Yunjae Lee, Juntaek Lim, Jehyeon Bang, Eunyeong Cho, Huijong Jeong, Taesu Kim, Hyungjun Kim, Joonhyung Lee, Jinseop Im, Ranggi Hwang, Se Jung Kwon, Dongsoo Lee, Minsoo Rhu

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Tags: AI, Benchmarking, Computer science, CUDA, Intel, Intel Gaudi-2, nVidia, nVidia A100, Performance

January 6, 2025 by hgpu

A comparison of HPC-based quantum computing simulators using Quantum Volume

Lourens van Niekerk, Dhiraj Kumar, Aasish Kumar Sharma, Tino Meisel, Martin Leandro Paleico, Christian Boehme

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Tags: Benchmarking, CUDA, nVidia, nVidia A100, OpenCL, Overview, Physics, Quantum computing, Review

January 6, 2025 by hgpu

Utilizing Tensor Cores in Futhark

Kristoffer August Kortbæk, Rune Ejnar Bang Lejbølle

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, High-level Languages, Matrix multiplication, nVidia, nVidia A100, Package, PTX

December 24, 2024 by hgpu

PyOMP: Parallel programming for CPUs and GPUs with OpenMP and Python

Giorgis Georgakoudis, Todd A. Anderson, Stuart Archibald, Bronis de Supinski,Timothy G. Mattson

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Tags: Benchmarking, Computer science, nVidia, nVidia A100, OpenMP, Package, Python

December 1, 2024 by hgpu

Context Parallelism for Scalable Million-Token Inference

Amy (Jie) Yang, Jingyi Yang, Aya Ibrahim, Xinfeng Xie, Bangsheng Tang, Grigory Sizov, Jeremy Reizenstein, Jongsoo Park, Jianyu Huang

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Tags: Artificial intelligence, Benchmarking, Computer science, LLM, nVidia, nVidia H100, Performance

November 17, 2024 by hgpu

The Rewriting of DataRaceBench Benchmark for OpenCL Program Validations

Chia-Chen Hsu, Chun-Lin Huang, Chao-Lin Lee, Jenq-Kuen Lee, Pei-Hung Lin

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Tags: Benchmarking, Computer science, LLVM, OpenCL, Performance

November 17, 2024 by hgpu

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high performance computing on graphics processing units: hgpu.org

Forecasting time series with constraints

Modernization and Optimization of MPI Codes

Dissecting the NVIDIA Hopper Architecture through Microbenchmarking and Multiple Level Analysis

LeetDecoding: A PyTorch Library for Exponentially Decaying Causal Linear Attention with CUDA Implementations

Debunking the CUDA Myth Towards GPU-based AI Systems

A comparison of HPC-based quantum computing simulators using Quantum Volume

Utilizing Tensor Cores in Futhark

PyOMP: Parallel programming for CPUs and GPUs with OpenMP and Python

Context Parallelism for Scalable Million-Token Inference

The Rewriting of DataRaceBench Benchmark for OpenCL Program Validations

Recent source codes

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Bandicoot: C++ library for GPU accelerated linear algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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