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Machine Learning-Driven Adaptive OpenMP For Portable Performance on Heterogeneous Systems

Giorgis Georgakoudis, Konstantinos Parasyris, Chunhua Liao, David Beckingsale, Todd Gamblin, Bronis de Supinski

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Tags: AMD Radeon Instinct Mi50, ATI, Benchmarking, Code generation, Compilers, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, OpenMP, performance portability, Tesla P100, Tesla V100

March 19, 2023 by hgpu

BenchDirect: A Directed Language Model for Compiler Benchmarks

Foivos Tsimpourlas, Pavlos Petoumenos, Min Xu, Chris Cummins, Kim Hazelwood, Ajitha Rajan, Hugh Leather

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Tags: Benchmarking, Compilers, Computer science, nVidia, nVidia GeForce GTX 1080, OpenCL, Package

March 12, 2023 by hgpu

ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills

Jiayuan Gu, Fanbo Xiang, Xuanlin Li, Zhan Ling, Xiqiang Liu, Tongzhou Mu, Yihe Tang, Stone Tao, Xinyue Wei, Yunchao Yao, Xiaodi Yuan, Pengwei Xie, Zhiao Huang, Rui Chen, Hao Su

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia Titan RTX, Package, Rendering, Robotics

February 12, 2023 by hgpu

A Symbolic Emulator for Shuffle Synthesis on the NVIDIA PTX Code

Kazuaki Matsumura, Simon Garcia De Gonzalo, Antonio J. Peña

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Tags: Benchmarking, Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, OpenACC, Package, PTX, Tesla K40, Tesla K80, Tesla V100

February 5, 2023 by hgpu

OpenMP Advisor

Alok Mishra, Abid M. Malik, Meifeng Lin, Barbara Chapman

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Tags: AMD Radeon Instinct Mi50, ATI, Benchmarking, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce RTX 2080, OpenMP, Tesla A100, Tesla K80, Tesla V100

January 15, 2023 by hgpu

Assessing Application Efficiency and Performance Portability in Single-Source Programming for Heterogeneous Parallel Systems

August Ernstsson, Dalvan Griebler, Christoph Kessler

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, OpenMP, Package, performance portability, Tesla K20

December 11, 2022 by hgpu

SAIH: A Scalable Evaluation Methodology for Understanding AI Performance Trend on HPC Systems

Jiangsu Du, Dongsheng Li, Yingpeng Wen, Jiazhi Jiang, Dan Huang, Xiangke Liao, Yutong Lu

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Tags: Artificial intelligence, Benchmarking, Computer science, CUDA, Deep learning, HPC, nVidia, PyTorch, Tesla V100

December 11, 2022 by hgpu

Training a Vision Transformer from scratch in less than 24 hours with 1 GPU

Saghar Irandoust, Thibaut Durand, Yunduz Rakhmangulova, Wenjie Zi, Hossein Hajimirsadeghi

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Tags: Benchmarking, Computer science, Computer vision, Deep learning

November 20, 2022 by hgpu

Challenges and Techniques for Transparent Acceleration of Unmodified Big Data Applications

Maria Nektaria Xekalaki

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Tags: Benchmarking, Computer science, CUDA, FPGA, Heterogeneous systems, Java, nVidia, nVidia GeForce GTX 1060, nVidia Quadro GP100, OpenCL, Package, Performance, Tesla V100, Thesis

November 20, 2022 by hgpu

Capturing the Memory Topology of GPUs

Dominik Größler

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce 840 M, nVidia GeForce RTX 2080 Ti, nVidia Quadro P 6000, Package, Performance, PTX, Tesla K20, Tesla V100, Thesis

November 13, 2022 by hgpu

Apple Silicon Performance in Scientific Computing

Connor Kenyon, Collin Capano

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Tags: Benchmarking, Computer science, Heterogeneous systems, nVidia, nVidia A100, nVidia V100, OpenCL, Performance

November 6, 2022 by hgpu

A systematic performance study of the parallel programming framework SkePU 3 using HPC-benchmarks

Erik Tedhamre

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Tags: Benchmarking, Computer science, CUDA, HPC, nVidia, OpenCL, OpenMP, Package, Performance

October 30, 2022 by hgpu

AutoDock-GPU: AutoDock for GPUs and other accelerators

Architecting Tensor Core-Based Reductions for Irregular Molecular Docking Kernels

NCCLX: collective communication framework

Collective Communication for 100k+ GPUs

Tutoring LLM into a Better CUDA Optimizer

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

A Performance Portable Matrix Free Dense MTTKRP in GenTen

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

ConCuR: Conciseness Makes State-of-the-Art Kernel Generation

See all packages

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high performance computing on graphics processing units: hgpu.org

Machine Learning-Driven Adaptive OpenMP For Portable Performance on Heterogeneous Systems

BenchDirect: A Directed Language Model for Compiler Benchmarks

ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills

A Symbolic Emulator for Shuffle Synthesis on the NVIDIA PTX Code

OpenMP Advisor

Assessing Application Efficiency and Performance Portability in Single-Source Programming for Heterogeneous Parallel Systems

SAIH: A Scalable Evaluation Methodology for Understanding AI Performance Trend on HPC Systems

Training a Vision Transformer from scratch in less than 24 hours with 1 GPU

Challenges and Techniques for Transparent Acceleration of Unmodified Big Data Applications

Capturing the Memory Topology of GPUs

Apple Silicon Performance in Scientific Computing

A systematic performance study of the parallel programming framework SkePU 3 using HPC-benchmarks

Recent source codes

AutoDock-GPU: AutoDock for GPUs and other accelerators

NCCLX: collective communication framework

Tutoring LLM into a Better CUDA Optimizer

Kernel Library for LLM Serving

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

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