high performance computing on graphics processing units: hgpu.org

Yehia Arafa, Ammar ElWazir, Abdelrahman ElKanishy, Youssef Aly, Ayatelrahman Elsayed, Abdel-Hameed Badawy, Gopinath Chennupati, Stephan Eidenbenz, Nandakishore Santhi

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Tags: Computer science, CUDA, Energy-efficient computing, Measurement techniques, nVidia, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX Titan V, nVidia GeForce GTX Titan X, nVidia Titan RTX, PTX

February 23, 2020 by hgpu

Instructions’ Latencies Characterization for NVIDIA GPGPUs

Yehia Arafa, Abdel-Hameed Badawy, Gopinath Chennupati, Nandakishore Santhi, Stephan Eidenbenz

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, nVidia Titan RTX, Performance, PTX, Tesla K40, Tesla P100, Tesla V100

May 23, 2019 by hgpu

CUDA au Coq: A Framework for Machine-validating GPU Assembly Programs

Benjamin Ferrell, Jun Duan, Kevin W. Hamlen

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Tags: Algorithms, Compilers, Computer science, CUDA, nVidia, PTX

May 15, 2019 by hgpu

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KLARAPTOR: A Tool for Dynamically Finding Optimal Kernel Launch Parameters Targeting CUDA Programs

Three Contributions to the Theory and Practice of Optimizing Compilers

HETU: a high-performance distributed deep learning system targeting large-scale and automated distributed training

Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

TorchOpt: an efficient library for differentiable optimization built upon PyTorch

TorchOpt: An Efficient Library for Differentiable Optimization

NaturalCC: An Open-Source Toolkit for Code Intelligence

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

mt4g: Memory Topology for GPUs

Capturing the Memory Topology of GPUs

See all packages

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high performance computing on graphics processing units: hgpu.org

Capturing the Memory Topology of GPUs

Demystifying the Nvidia Ampere Architecture through Microbenchmarking and Instruction-level Analysis

Dissecting Tensor Cores via Microbenchmarks: Latency, Throughput and Numerical Behaviors

Research and Development of Porting SYCL on QNX Operating System for High Parallelism

Improving Performance and Energy Efficiency of GPUs through Locality Analysis

LocalityGuru: A PTX Analyzer for Extracting Thread Block-level Locality in GPGPUs

SYCL-Bench: A Versatile Cross-Platform Benchmark Suite for Heterogeneous Computing

Automatic Kernel Generation for Volta Tensor Cores

GEVO: GPU Code Optimization using Evolutionary Computation

Verified Instruction-Level Energy Consumption Measurement for NVIDIA GPUs

Instructions’ Latencies Characterization for NVIDIA GPGPUs

CUDA au Coq: A Framework for Machine-validating GPU Assembly Programs

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SW#SYCL

Flink-TornadoVM-Artifact

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