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Mixing Low-Precision Formats in Multiply-Accumulate Units for DNN Training

Mariko Tatsumi, Silviu-Ioan Filip, Caroline White, Olivier Sentieys, Guy Lemieux

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Tags: Computer science, CUDA, Deep learning, FPGA, nVidia, Precision

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

Three Contributions to the Theory and Practice of Optimizing Compilers

Linxiao Wang

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Tags: Compilers, Computer science, CUDA, LLVM, nVidia, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX 760 M, Package, Thesis

December 4, 2022 by hgpu

Fast convolution kernels on pascal GPU with high memory efficiency

Qiong Chang, Masaki Onishi, Tsutomu Maruyama

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Tags: CNN, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 1080 Ti, Performance

December 4, 2022 by hgpu

Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

Xupeng Miao, Yujie Wang, Youhe Jiang, Chunan Shi, Xiaonan Nie, Hailin Zhang, Bin Cui

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Tags: Computer science, CUDA, Databases, Deep learning, nVidia, nVidia Titan RTX, Package

December 4, 2022 by hgpu

A Hybrid Multi-GPU Implementation of Simplex Algorithm with CPU Collaboration

Basilis Mamalis, Marios Perlitis

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce RTX 2080 Ti, OpenMP

November 27, 2022 by hgpu

Assessing Opportunities of SYCL and Intel oneAPI for Biological Sequence Alignment

Manuel Costanzo, Enzo Rucci, Carlos García Sánchez, Marcelo Naiouf, Manuel Prieto-Matías

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Tags: Bioinformatics, Biology, Computational biology, Computer science, CUDA, Heterogeneous systems, Intel, Intel UHD 630, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980, nVidia GeForce RTX 2070, nVidia GeForce RTX 3090, OpenCL, Sequence alignment, SYCL, Tesla V100

November 27, 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

Going green: optimizing GPUs for energy efficiency through model-steered auto-tuning

Richard Schoonhoven, Bram Veenboer, Ben van Werkhoven, Kees Joost Batenburg

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia RTX A4000, OpenCL, Package, Tesla A100

November 20, 2022 by hgpu

TorchOpt: An Efficient Library for Differentiable Optimization

Jie Ren, Xidong Feng, Bo Liu, Xuehai Pan, Yao Fu, Luo Mai, Yaodong Yang

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Tags: Computer science, CUDA, Deep learning, Mathematical Software, nVidia, OpenMP, Package, PyTorch

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

A Study on Neural-based Code Summarization in Low-resource Settings

Yang He

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Tags: Computer science, CUDA, Deep learning, nVidia, PyTorch, Software Engineering, Tesla V100, Thesis

November 13, 2022 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

Mixing Low-Precision Formats in Multiply-Accumulate Units for DNN Training

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

Three Contributions to the Theory and Practice of Optimizing Compilers

Fast convolution kernels on pascal GPU with high memory efficiency

Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

A Hybrid Multi-GPU Implementation of Simplex Algorithm with CPU Collaboration

Assessing Opportunities of SYCL and Intel oneAPI for Biological Sequence Alignment

Challenges and Techniques for Transparent Acceleration of Unmodified Big Data Applications

Going green: optimizing GPUs for energy efficiency through model-steered auto-tuning

TorchOpt: An Efficient Library for Differentiable Optimization

Capturing the Memory Topology of GPUs

A Study on Neural-based Code Summarization in Low-resource Settings

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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