high performance computing on graphics processing units: hgpu.org

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Profiling High Level Heterogeneous Programs: Using the SPOC GPGPU framework for OCaml

Mathias Bourgoin, Emmanuel Chailloux, Anastasios Doumoulakis

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Package, Performance

February 18, 2017 by hgpu

LAMMPS’ PPPM Long-Range Solver for the Second Generation Xeon Phi

William McDoniel, Markus Hohnerbach, Rodrigo Canales, Ahmed E. Ismail, Paolo Bientinesi

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Tags: Chemistry, Computer science, Intel Xeon Phi, Molecular dynamics, Performance

February 18, 2017 by hgpu

Best Practice Guide Intel Xeon Phi v2.0

Emanouil Atanassov, Michaela Barth, Mikko Byckling, Vali Codreanu, Nevena Ilieva, Tomas Karasek, Jorge Rodriguez, Sami Saarinen, Ole Widar Saastad, Michael Schliephake, Martin Stachon, Janko Strassburg, Volker Weinberg (Editor)

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Tags: Benchmarking, Computer science, Intel Xeon Phi, OpenMP, Performance

February 14, 2017 by hgpu

Improving the Performance of Fully Connected Neural Networks by Out-of-Place Matrix Transpose

Shaohuai Shi, Pengfei Xu, Xiaowen Chu

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Tags: Algorithms, BLAS, Caffe, Computer science, CUBLAS, CUDA, Deep learning, Linear Algebra, Matrix multiplication, Neural networks, nVidia, nVidia GeForce GTX 1080, Package, Performance

February 14, 2017 by hgpu

Accelerating Binarized Neural Networks: Comparison of FPGA, CPU, GPU, and ASIC

Eriko Nurvitadhi, David Sheffield, Jaewoong Sim, Asit Mishra, Ganesh Venkatesh, Debbie Marr

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Tags: Algorithms, ASIC, Computer science, CUDA, Deep learning, FPGA, Neural networks, nVidia, nVidia GeForce GTX Titan X, nVidia Tegra TX1, Performance

February 14, 2017 by hgpu

Autotuning GPU Kernels via Static and Predictive Analysis

Robert V. Lim, Boyana Norris, Allen D. Malony

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Tags: Code generation, Computer science, CUDA, nVidia, Performance, PTX, Tesla K20, Tesla M2050, Tesla M40

February 2, 2017 by hgpu

Parallel Implementations of the Cholesky Decomposition on CPUs and GPUs

Joao Paulo Tarasconi Ruschel

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Linear Algebra, Matrix decomposition, nVidia, OpenCL, OpenMP, Package, Performance, Tesla K80, Thesis

January 26, 2017 by hgpu

GPGPU Performance Estimation with Core and Memory Frequency Scaling

Qiang Wang, Xiaowen Chu

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Tags: Computer science, CUDA, Hardware, nVidia, nVidia GeForce GTX 980, Performance

January 23, 2017 by hgpu

An OpenCL(TM) Deep Learning Accelerator on Arria 10

Utku Aydonat, Shane O'Connell, Davor Capalija, Andrew C. Ling, Gordon R. Chiu

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Tags: CNN, Computer science, Computer vision, Deep learning, FPGA, OpenCL, Performance

January 16, 2017 by hgpu

An N log N Parallel Fast Direct Solver for Kernel Matrices

Chenhan D. Yu, William B. March, George Biros

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Tags: Algorithms, Computational Complexity, Computer science, Factorization, Intel Xeon Phi, Machine learning, Package, Performance

January 16, 2017 by hgpu

An FPGA Accelerator for Molecular Dynamics Simulation Using OpenCL

Hasitha Muthumala Waidyasooriya, Masanori Hariyama, Kota Kasahara

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Tags: Chemistry, FPGA, Heterogeneous systems, Molecular dynamics, OpenCL, Performance, Physics

January 10, 2017 by hgpu

An initial performance review of software components for a heterogeneous computing platform

Ivan Svogor

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Tags: ATI, ATI Radeon HD 7750, Computer science, Energy-efficient computing, FPGA, Heterogeneous systems, OpenCL, Performance, Robotics

January 4, 2017 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

Profiling High Level Heterogeneous Programs: Using the SPOC GPGPU framework for OCaml

LAMMPS’ PPPM Long-Range Solver for the Second Generation Xeon Phi

Best Practice Guide Intel Xeon Phi v2.0

Improving the Performance of Fully Connected Neural Networks by Out-of-Place Matrix Transpose

Accelerating Binarized Neural Networks: Comparison of FPGA, CPU, GPU, and ASIC

Autotuning GPU Kernels via Static and Predictive Analysis

Parallel Implementations of the Cholesky Decomposition on CPUs and GPUs

GPGPU Performance Estimation with Core and Memory Frequency Scaling

An OpenCL(TM) Deep Learning Accelerator on Arria 10

An N log N Parallel Fast Direct Solver for Kernel Matrices

An FPGA Accelerator for Molecular Dynamics Simulation Using OpenCL

An initial performance review of software components for a heterogeneous computing platform

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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