high performance computing on graphics processing units: hgpu.org

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Using Intel oneAPI for Multi-hybrid Acceleration Programming with GPU and FPGA Coupling

Wentao Liang, Norihisa Fujita, Ryohei Kobayashi, Taisuke Boku

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, oneAPI, OpenCL, SYCL, Tesla V100

April 7, 2024 by hgpu

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

Erik D. Huckvale, Hunter N.B. Moseley

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Tags: Computer science, CUDA, nVidia, Package, Performance, Profiling, Python

April 7, 2024 by hgpu

Retargeting and Respecializing GPU Workloads for Performance Portability

Ivan R. Ivanov, Oleksandr Zinenko, Jens Domke, Toshio Endo, William S. Moses

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Tags: AMD Radeon Instinct MI210, AMD Radeon RX 6800, ATI, Computer science, CUDA, HIP, HPC, nVidia, nVidia A100, nVidia RTX A4000, Package, performance portability

March 24, 2024 by hgpu

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

John Tramm, Paul Romano, Patrick Shriwise, Amanda Lund, Johannes Doerfert, Patrick Steinbrecher, Andrew Siegel, Gavin Ridley

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Tags: AMD Radeon Instinct MI250X, ATI, Computer science, CUDA, Intel, Intel Data Center GPU Max 1550, Intel Ponte Vecchio Max 1100, nVidia, nVidia A100, OpenMP, Package, performance portability

March 24, 2024 by hgpu

Parallel Gaussian process with kernel approximation in CUDA

Davide Carminati

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Tags: Benchmarking, Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 1050, nVidia GeForce RTX 2080, Package

March 24, 2024 by hgpu

Cost-Effective Methodology for Complex Tuning Searches in HPC: Navigating Interdependencies and Dimensionality

Adrian Perez Dieguez, Min Choi, Mahmut Okyay, Mauro Del Ben, Bryan M. Wong, Khaled Z. Ibrahim

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Tags: Computer science, CUDA, HPC, MPI, nVidia, nVidia A100, OpenMP, Performance

March 18, 2024 by hgpu

Fast Truncated SVD of Sparse and Dense Matrices on Graphics Processors

Andres E. Tomas, Enrique S. Quintana-Orti, Hartwig Anzt

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, nVidia A100

March 18, 2024 by hgpu

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

Youssef Faqir-Rhazoui, Carlos García

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Tags: Computer science, CUDA, nVidia, nVidia Jetson Orin Nano, Optical flow, Package, Performance, SYCL

March 18, 2024 by hgpu

Parallel Implementation of Lightweight Secure Hash Algorithm on CPU and GPU Environments

Hojin Choi, SeongJun Choi, SeogChung Seo

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Tags: Computer science, CUDA, Hashing, nVidia, nVidia GeForce RTX 2080 Ti, nVidia GeForce RTX 3090, Package, PTX, Security

March 10, 2024 by hgpu

Using AI libraries for Incompressible Computational Fluid Dynamics

Boyang Chen, Claire E. Heaney, Christopher C. Pain

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Tags: AI, Artificial intelligence, cfd, CUDA, Fluid dynamics, Neural networks, nVidia, Partial differential equations, PDEs, Tesla T4

March 3, 2024 by hgpu

Spyx: A Library for Just-In-Time Compiled Optimization of Spiking Neural Networks

Kade M. Heckel, Thomas Nowotny

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Tags: Artificial intelligence, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia RTX A6000, Package, Python

March 3, 2024 by hgpu

Low-Overhead Trace Collection and Profiling on GPU Compute Kernels

Sébastien Darche, Michel R. Dagenais

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Tags: AMD Radeon Instinct MI100, ATI, Benchmarking, Computer science, CUDA, HIP, nVidia, Package, Performance, Profiling

March 3, 2024 by hgpu

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

Generating Literature-Driven Scientific Theories at Scale

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high performance computing on graphics processing units: hgpu.org

Programming

Using Intel oneAPI for Multi-hybrid Acceleration Programming with GPU and FPGA Coupling

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

Retargeting and Respecializing GPU Workloads for Performance Portability

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Parallel Gaussian process with kernel approximation in CUDA

Cost-Effective Methodology for Complex Tuning Searches in HPC: Navigating Interdependencies and Dimensionality

Fast Truncated SVD of Sparse and Dense Matrices on Graphics Processors

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

Parallel Implementation of Lightweight Secure Hash Algorithm on CPU and GPU Environments

Using AI libraries for Incompressible Computational Fluid Dynamics

Spyx: A Library for Just-In-Time Compiled Optimization of Spiking Neural Networks

Low-Overhead Trace Collection and Profiling on GPU Compute Kernels

Recent source codes

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Vortex-Optimized Light-weight Toolchain (VOLT)

SciDef: Automated Definition Extraction from Scientific Literature

bioagent-bench: Benchmark for evaluating LLM agents in bioinformatics

Benchmark suite for LLM inference on NVIDIA consumer GPUs

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

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