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

Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations

Ezhilmathi Krishnasamy, Pascal Bouvry

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Tags: BLAS, Computer science, CUDA, Differential equations, HPC, Linear Algebra, Matrix multiplication, nVidia, nVidia A100, OpenACC, Package, Partial differential equations, PDEs, Performance

December 24, 2024 by hgpu

Towards Performance-Aware Allocation for Accelerated Machine Learning on GPU-SSD Systems

Ayush Gundawar, Euijun Chung, Hyesoon Kim

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Tags: Computer science, Hardware Architecture, Machine learning, Performance

December 15, 2024 by hgpu

Automating Energy-Efficient GPU Kernel Generation: A Fast Search-Based Compilation Approach

Yijia Zhang, Zhihong Gou, Shijie Cao, Weigang Feng, Sicheng Zhang, Guohao Dai, Ningyi Xu

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Tags: Computer science, CUDA, Energy-efficient computing, Machine learning, Neural networks, nVidia, nVidia A100, nVidia GeForce RTX 4090, Performance

December 8, 2024 by hgpu

Hardware Accelerators for Artificial Intelligence

S M Mojahidul Ahsan, Anurag Dhungel, Mrittika Chowdhury, Md Sakib Hasan, Tamzidul Hoque

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Tags: AI, Computer science, Hardware Architecture, Performance, survey

December 1, 2024 by hgpu

Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach

Xiaoteng (Frank) Liu, Pavly Halim

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Tags: Computer science, CUDA, Energy-efficient computing, GEMM, Machine learning, Matrix multiplication, nVidia, nVidia GeForce RTX 4070, Package, Performance

December 1, 2024 by hgpu

Performance portability via C++ PSTL, SYCL, OpenMP, and HIP: the Gaia AVU-GSR case study

Giulio Malenza, Valentina Cesare, Marco Edoardo Santimaria, Robert Birke, Alberto Vecchiato, Ugo Becciani, Marco Aldinucci

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Tags: AMD Radeon Instinct MI250X, Astrophysics, ATI, Computer science, CUDA, HIP, HPC, nVidia, nVidia A100, nVidia H100, nVidia V100, OpenMP, Package, Performance, performance portability, SYCL, Tesla T4

November 24, 2024 by hgpu

UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization

CuFuzz: An API-Knowledge-Graph Coverage-Driven Fuzzing Framework for CUDA Libraries

AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

Analyzing the Impact of Kernel Fusion on GPU Tensor Operation Performance: A Systematic Performance Study

IntelliKit: Agent-first tooling for AMD hardware

Kerncap: Automated Kernel Extraction and Isolation for AMD GPUs

DITRON: Distributed Compiler based on Triton for Parallel Systems

DITRON: Distributed Multi-level Tiling Compiler for Parallel Tensor Programs

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high performance computing on graphics processing units: hgpu.org

Modernization and Optimization of MPI Codes

Profiling Apple Silicon Performance for ML Training

Adaptive Optimization Techniques for High-Performance Computing

Dissecting the NVIDIA Hopper Architecture through Microbenchmarking and Multiple Level Analysis

Boosting Performance of Iterative Applications on GPUs: Kernel Batching with CUDA Graphs

Debunking the CUDA Myth Towards GPU-based AI Systems

Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations

Towards Performance-Aware Allocation for Accelerated Machine Learning on GPU-SSD Systems

Automating Energy-Efficient GPU Kernel Generation: A Fast Search-Based Compilation Approach

Hardware Accelerators for Artificial Intelligence

Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach

Performance portability via C++ PSTL, SYCL, OpenMP, and HIP: the Gaia AVU-GSR case study

Recent source codes

UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization

CuFuzz: An API-Knowledge-Graph Coverage-Driven Fuzzing Framework for CUDA Libraries

AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning

Probe-and-Refine Tuning of Repository Guidance for AI Coding Agents

CUDAnalyst (CUDA + Analyst)

CodegenBench

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

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