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Enhancing Code Portability, Problem Scale, and Storage Efficiency in Exascale Applicationsin Exascale Applications

Nigel Tan

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Tags: AMD Radeon Vega VII, ATI, Computer science, CUDA, Heterogeneous systems, HPC, nVidia, nVidia A100, nVidia DGX-A100, nVidia Quadro RTX 5000, OpenMP, OpenMPI, Particle-in-cell methods, performance portability, Tesla V100, Thesis

September 15, 2024 by hgpu

Abstractions for C++ code optimizations in parallel high-performance applications

Jiří Klepl, Adam Šmelko, Lukáš Rozsypal, Martin Kruliš

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Tags: Auto-Tuning, Computer science, CUDA, Data Structures and Algorithms, HPC, nVidia, Package, Tesla V100

August 25, 2024 by hgpu

Evaluating Operators in Deep Neural Networks for Improving Performance Portability of SYCL

Zheming Jin

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Tags: Benchmarking, Computer science, CUDA, HIP, Machine learning, Neural networks, nVidia, nVidia GeForce RTX 2080, nVidia GeForce RTX 3090, nVidia H100, oneAPI, Performance, performance portability, SYCL, Tesla A100, Tesla V100

August 14, 2024 by hgpu

LO-SpMM: Low-cost Search for High-performance SpMM Kernels on GPUs

Junqing Lin, Jingwei Sun, Xiaolong Shi, Honghe Zhang, Xianzhi Yu, Xinzhi Wang, Jun Yao, Guangzhong Sun

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Tags: Compilers, Computer science, CUDA, Deep learning, Linear Algebra, Matrix multiplication, Neural networks, nVidia, nVidia GeForce RTX 2080 Ti, Performance, Sparse matrix, Tesla V100

August 4, 2024 by hgpu

RBMD: A molecular dynamics package enabling to simulate 10 million all-atom particles in a single graphics processing unit

Weihang Gao, Teng Zhao, Yongfa Guo, Jiuyang Liang, Huan Liu, Maoying Luo, Zedong Luo, Wei Qin, Yichao Wang, Qi Zhou, Shi Jin, Zhenli Xu

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Tags: Computational Physics, CUDA, Heterogeneous systems, Molecular dynamics, nVidia, nVidia GeForce RTX 4090, Package, Physics, Tesla A100, Tesla V100

July 28, 2024 by hgpu

Optimization of Large-Scale Sparse Matrix-Vector Multiplication on Multi-GPU Systems

Jianhua Gao, Weixing Ji, Yizhuo Wang

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Tags: Computer science, CUDA, Linear Algebra, nVidia, Optimization, Sparse matrix, Tesla V100

July 14, 2024 by hgpu

Automating Heterogeneous Parallelism in Numerical Differential Equations

Utkarsh

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Tags: AMD RX Vega 64, ATI, Computer science, CUDA, Differential equations, Factorization, Heterogeneous systems, Intel, Intel Arc A770, Julia, Numerical Analysis, nVidia, nVidia Quadro RTX 5000, Package, Tesla V100, Thesis

July 14, 2024 by hgpu

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

SYCL-Bench 2020: Benchmarking SYCL 2020 on AMD, Intel, and NVIDIA GPUs

Luigi Crisci, Lorenzo Carpentieri, Peter Thoman, Aksel Alpay, Vincent Heuveline, Biagio Cosenza

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Tags: AMD Radeon Instinct MI100, ATI, Benchmarking, Computer science, Heterogeneous systems, HPC, Intel, Intel Ponte Vecchio Max 1100, nVidia, oneAPI, Portability, SYCL, Tesla V100

March 10, 2024 by hgpu

Assessing the Impact of Compiler Optimizations on GPUs Reliability

Fernando Fernandes dos Santos, Luigi Carro, Flavio Vella, Paolo Rech

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Tags: Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan V, PTX, Tesla K40, Tesla V100

January 28, 2024 by hgpu

Code Generation for a Variety of Accelerators for a Graph DSL

Ashwina Kumar, M. Venkata Krishna, Prasanna Bartakke, Rahul Kumar, Rajesh Pandian M, Nibedita Behera, Rupesh Nasre

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Tags: Code generation, Computer science, CUDA, DSL, nVidia, nVidia GeForce RTX 2080 Ti, OpenACC, OpenCL, Package, SYCL, Tesla V100

January 14, 2024 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

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

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high performance computing on graphics processing units: hgpu.org

Enhancing Code Portability, Problem Scale, and Storage Efficiency in Exascale Applicationsin Exascale Applications

Abstractions for C++ code optimizations in parallel high-performance applications

Evaluating Operators in Deep Neural Networks for Improving Performance Portability of SYCL

LO-SpMM: Low-cost Search for High-performance SpMM Kernels on GPUs

RBMD: A molecular dynamics package enabling to simulate 10 million all-atom particles in a single graphics processing unit

Optimization of Large-Scale Sparse Matrix-Vector Multiplication on Multi-GPU Systems

Automating Heterogeneous Parallelism in Numerical Differential Equations

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

SYCL-Bench 2020: Benchmarking SYCL 2020 on AMD, Intel, and NVIDIA GPUs

Assessing the Impact of Compiler Optimizations on GPUs Reliability

Code Generation for a Variety of Accelerators for a Graph DSL

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

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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