high performance computing on graphics processing units: hgpu.org

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pSTL-Bench: A Micro-Benchmark Suite for Assessing Scalability of C++ Parallel STL Implementations

Ruben Laso, Diego Krupitza, Sascha Hunold

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia Ampere A2, OpenMP, performance portability, Tesla P4

February 18, 2024 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

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

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

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

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

Efficiently Processing Large Relational Joins on GPUs

Bowen Wu, Dimitrios Koutsoukos, Gustavo Alonso

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Tags: Benchmarking, Computer science, CUDA, Databases, nVidia, nVidia A100, nVidia GeForce RTX 3090, Package

December 10, 2023 by hgpu

Testing and Mutation Testing for GPU Kernels

Yujie Liu

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Tags: Benchmarking, Computer science, CUDA, nVidia, Package, Performance, Thesis

December 3, 2023 by hgpu

Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C+

Bin Lei, Caiwen Ding, Le Chen, Pei-Hung Lin, Chunhua Liao

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Tags: Benchmarking, Computer science, CUDA, Fortran, Machine learning, nVidia, nVidia RTX A6000, OpenMP, Package

November 19, 2023 by hgpu

An approach to performance portability through generic programming

Andreas Hadjigeorgiou, Christodoulos Stylianou, Michele Weiland, Dirk Jacob Verschuur, Jacob Finkenrath

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Tags: AMD Radeon Instinct MI100, ATI, Benchmarking, Computer science, CUDA, HIP, HPC, nVidia, OpenMP, Package, Performance, performance portability, Portability, Tesla V100

November 12, 2023 by hgpu

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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high performance computing on graphics processing units: hgpu.org

pSTL-Bench: A Micro-Benchmark Suite for Assessing Scalability of C++ Parallel STL Implementations

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

Comparative Performance and Scalability Analysis of GPU-accelerated Database Operations

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

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

Principles for Automated and Reproducible Benchmarking

Efficiently Processing Large Relational Joins on GPUs

Testing and Mutation Testing for GPU Kernels

Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C+

An approach to performance portability through generic programming

Recent source codes

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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