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Performance Evaluation of Heterogeneous GPU Programming Frameworks for Hemodynamic Simulations

Aristotle Martin, Geng Liu, William Ladd, Seyong Lee, John Gounley, Jeffrey Vetter, Saumil Patel, Silvio Rizzi, Victor Mateevitsi, Joseph Insley, Amanda Randles

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Tags: AMD Radeon Instinct MI250X, ATI, cfd, CUDA, Fluid dynamics, Heterogeneous systems, HIP, nVidia, nVidia A100, nVidia V100, performance portability, SYCL

December 31, 2023 by hgpu

Enabling Quantum Computer Simulations on AMD GPUs: a HIP Backend for Google’s qsim

Stefano Markidis

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Tags: AMD Radeon Instinct MI250X, Computer science, CUDA, HIP, nVidia, nVidia A100, Quantum computing

December 31, 2023 by hgpu

Gaiwan: a Size-Polymorphic Typesystem for GPU Programs

Robbert Gurdeep Singha, Christophe Scholliers

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Tags: Benchmarking, Computer science, nVidia, nVidia A100, OpenCL, Package, Programming Languages

December 31, 2023 by hgpu

KeSCo: Compiler-based Kernel Scheduling for Multi-task GPU Applications

Zejia Lin, Zewei Mo, Xuanteng Huang, Xianwei Zhang, Yutong Lu

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Tags: Benchmarking, Compilers, Computer science, CUDA, nVidia, nVidia A100, Task scheduling

December 24, 2023 by hgpu

FCBench: Cross-Domain Benchmarking of Lossless Compression for Floating-Point Data

Xinyu Chen, Jiannan Tian, Ian Beaver, Cynthia Freeman, Yan Yan, Jianguo Wang, Dingwen Tao

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Tags: Benchmarking, Compression, Computer science, CUDA, Databases, HPC, nVidia, nVidia RTX 6000 Ada, Package, SYCL

December 24, 2023 by hgpu

Experiences Building an MLIR-based SYCL Compiler

Ettore Tiotto, Víctor Pérez, Whitney Tsang, Lukas Sommer, Julian Oppermann, Victor Lomüller, Mehdi Goli, James Brodman

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Tags: Compilers, Computer science, Heterogeneous systems, Performance, Programming Languages, SYCL

December 24, 2023 by hgpu

Binary Code Summarization: Benchmarking ChatGPT/GPT-4 and Other Large Language Models

Xin Jin, Jonathan Larson, Weiwei Yang, Zhiqiang Lin

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Tags: Benchmarking, Computer science, nVidia, nVidia A100, Software Engineering

December 24, 2023 by hgpu

Comparative Performance and Scalability Analysis of GPU-accelerated Database Operations

Carl Andersson, Jonathan Nilsson

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Tags: Benchmarking, Computer science, CUDA, Databases, nVidia, nVidia A10, nVidia M60, Performance, Tesla T4, Thesis

December 24, 2023 by hgpu

Scalable Tuning of (OpenMP) GPU Applications via Kernel Record and Replay

Konstantinos Parasyris, Giorgis Georgakoudis, Esteban Rangel, Ignacio Laguna, Johannes Doerfert

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Tags: AMD Radeon Instinct MI250X, ATI, Auto-Tuning, Computer science, Heterogeneous systems, nVidia, nVidia V100, OpenMP, Package

December 18, 2023 by hgpu

Precision and Performance Analysis of C Standard Math Library Functions on GPUs

Anton Rydahl, Joseph Huber, Ethan Luis McDonough, Johannes Doerfert

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Tags: AMD Radeon Instinct MI250X, AMD Radeon Instinct Mi50, ATI, Benchmarking, Compilers, Computer science, CUDA, HIP, Numerical Analysis, OpenMP, Performance, Tesla A100, Tesla V100

December 18, 2023 by hgpu

Application Performance Profiling on Intel GPUs with Oneprof and Onetrace

Mariam Umar, Maxwell Jong

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Tags: Computer science, Intel, Intel Data Center GPU Max 1550, OpenCL, OpenMP, Package, Performance

December 18, 2023 by hgpu

Principles for Automated and Reproducible Benchmarking

Tuomas Koskela, Ilektra Christidi, Mosè Giordano, Emily Dubrovska, Jamie Quinn, Christopher Maynard, Dave Case, Kaan Olgu, Tom Deakin

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Tags: Benchmarking, Computer science, HPC, nVidia, oneAPI, Package, performance portability

December 18, 2023 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

Applications

Performance Evaluation of Heterogeneous GPU Programming Frameworks for Hemodynamic Simulations

Enabling Quantum Computer Simulations on AMD GPUs: a HIP Backend for Google’s qsim

Gaiwan: a Size-Polymorphic Typesystem for GPU Programs

KeSCo: Compiler-based Kernel Scheduling for Multi-task GPU Applications

FCBench: Cross-Domain Benchmarking of Lossless Compression for Floating-Point Data

Experiences Building an MLIR-based SYCL Compiler

Binary Code Summarization: Benchmarking ChatGPT/GPT-4 and Other Large Language Models

Comparative Performance and Scalability Analysis of GPU-accelerated Database Operations

Scalable Tuning of (OpenMP) GPU Applications via Kernel Record and Replay

Precision and Performance Analysis of C Standard Math Library Functions on GPUs

Application Performance Profiling on Intel GPUs with Oneprof and Onetrace

Principles for Automated and Reproducible Benchmarking

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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