high performance computing on graphics processing units: hgpu.org

hgpu.org » PTX

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

Devashree Tripathy, AmirAli Abdolrashidi, Quan Fan, Daniel Wong, Manoranjan Satpathy

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Tags: Computer science, CUDA, nVidia, PTX

September 5, 2021 by hgpu

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

Sohan Lal, Aksel Alpay, Philip Salzmann, Biagio Cosenza, Alexander Hirsch, Nicolai Stawinoga, Peter Thoman, Thomas Fahringer, Vincent Heuveline

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Tags: Benchmarking, Computer science, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package, Performance, PTX, SYCL

July 5, 2020 by hgpu

Automatic Kernel Generation for Volta Tensor Cores

Somashekaracharya G. Bhaskaracharya, Julien Demouth, Vinod Grover

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Tags: Compilers, Computer science, CUBLAS, CUDA, Deep learning, Matrix multiplication, nVidia, nVidia Quadro GV100, Performance, Programming Languages, PTX

June 28, 2020 by hgpu

GEVO: GPU Code Optimization using Evolutionary Computation

Jhe-Yu Liou, Xiaodong Wang, Stephanie Forrest, Carole-Jean Wu

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Tags: Computer science, CUDA, Evolutionary Computations, Machine learning, Neural and Evolutionary Computing, nVidia, Package, PTX, Tesla P100

April 26, 2020 by hgpu

Verified Instruction-Level Energy Consumption Measurement for NVIDIA GPUs

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

Dissecting the NVidia Turing T4 GPU via Microbenchmarking

Zhe Jia, Marco Maggioni, Jeffrey Smith, Daniele Paolo Scarpazza

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Tags: Benchmarking, Computer science, CUDA, Deep learning, nVidia, Performance, PTX, Tesla P100, Tesla V100

March 24, 2019 by hgpu

Improving OpenCL Performance by Specializing Compiler Phase Selection and Ordering

Ricardo Nobre, Luis Reis, Joao M. P. Cardoso

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Tags: Computer science, nVidia, nVidia GeForce GTX 1070, OpenCL, Performance, PTX

October 28, 2018 by hgpu

Multidimensional Parallelization for Streaming Text Processing Applications Based on Parabix Framework

Dan Lin

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Tags: Algorithms, Computer science, Data parallelism, LLVM, nVidia, nVidia GeForce GTX 950, Package, PTX, Thesis

April 28, 2018 by hgpu

Dissecting the NVIDIA Volta GPU Architecture via Microbenchmarking

Zhe Jia, Marco Maggioni, Benjamin Staiger, Daniele P. Scarpazza

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Tags: Benchmarking, Computer science, CUDA, nVidia, Performance, PTX, Tesla V100

April 22, 2018 by hgpu

Technical Report about Tiramisu: a Three-Layered Abstraction for Hiding Hardware Complexity from DSL Compilers

Riyadh Baghdadi, Jessica Ray, Malek Ben Romdhane, Emanuele Del Sozzo, Patricia Suriana, Shoaib Kamil, Saman Amarasinghe

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Tags: Compilers, Computer science, DSL, FPGA, Matrix multiplication, nVidia, OpenMPI, Performance, Programming Languages, PTX, Tesla K40

March 3, 2018 by hgpu

NVPTX-SPIR-V Translator

Supporting CUDA for an extended RISC-V GPU architecture

Taichi Lattice Boltmann

Data-Oriented Language Implementation of Lattice-Boltzmann Method for Dense and Sparse Geometries

WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU

MLIR: Multi-Level Intermediate Representation

High Performance GPU Code Generation for Matrix-Matrix Multiplication using MLIR: Some Early Results

SeisSol: High Resolution Simulation os Seismic Wave Propagation in Realistic Media with Complex Geomerty

Performance Portability and Evaluation of Heterogeneous Components of SeisSol Targeted to Upcoming Intel HPC GPUs

perf4sight: A toolflow to model CNN training performance on Edge GPUs

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high performance computing on graphics processing units: hgpu.org

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

Dissecting the NVidia Turing T4 GPU via Microbenchmarking

Improving OpenCL Performance by Specializing Compiler Phase Selection and Ordering

Multidimensional Parallelization for Streaming Text Processing Applications Based on Parabix Framework

Dissecting the NVIDIA Volta GPU Architecture via Microbenchmarking

Technical Report about Tiramisu: a Three-Layered Abstraction for Hiding Hardware Complexity from DSL Compilers

Recent source codes

NVPTX-SPIR-V Translator

Taichi Lattice Boltmann

WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU

MLIR: Multi-Level Intermediate Representation

SeisSol: High Resolution Simulation os Seismic Wave Propagation in Realistic Media with Complex Geomerty

perf4sight: A toolflow to model CNN training performance on Edge GPUs

Parament: Parallelized Matrix Exponentiation for Numerical Time evolution

ndzip: A High-Throughput Parallel Lossless Compressor for Scientific Data

ScaleHLS Project

POCL: Portable Computing Language

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