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Predicting the Execution Time of a kernel on a specific GPU using PTX code

Monika Dagar, Jorge Roldan

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Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX 1650, nVidia GeForce GTX Titan XP, Performance, PTX, Tesla K20, Tesla K80, Tesla M60, Tesla P100, Tesla T4, Tesla V100

October 22, 2023 by hgpu

Prediction of Performance and Power Consumption of GPGPU Applications

Gargi Alavani, Santonu Sarkar

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Tags: Computer science, CUDA, Energy-efficient computing, Java, Machine learning, nVidia, Package, Performance, PTX, Tesla K20, Thesis

May 14, 2023 by hgpu

Evaluation of Rust for GPGPU high-performance computing

Viktor Franzén, Carl Östling

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Tags: Computer science, CUDA, HPC, nVidia, nVidia GeForce GTX 1660 Ti, nVidia GeForce RTX 2080 Ti, Package, PTX, Thesis

February 5, 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

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

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

Hamdy Abdelkhalik, Yehia Arafa, Nandakishore Santhi, Abdel-Hameed Badawy

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Tags: Benchmarking, Computer science, Hardware Architecture, Latency, nVidia, nVidia A100, Performance, PTX

August 28, 2022 by hgpu

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

Wei Sun, Ang Li, Tong Geng, Sander Stuijk, Henk Corporaal

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Tags: Benchmarking, Computer science, Matrix multiplication, nVidia, nVidia A100, nVidia GeForce RTX 2080, nVidia GeForce RTX 2080 Ti, Performance, PTX, Sparse matrix

June 12, 2022 by hgpu

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

Dengpan Wang

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Operating systems, PTX, SYCL, Thesis

January 16, 2022 by hgpu

Improving Performance and Energy Efficiency of GPUs through Locality Analysis

Devashree Tripathy

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX Titan V, nVidia GeForce GTX Titan X, Performance, PTX, Thesis

October 31, 2021 by hgpu

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

Performance Study of GPU-based Joins

Efficiently Processing Large Relational Joins on GPUs

GenVectorX: A performance-portable SYCL library for Lorentz Vectors operations

bib boi: Tools to help with LaTeX paper writing

Compiler-centric across-stack deep learning acceleration

Kernel Tuner

Testing and Mutation Testing for GPU Kernels

CuPBoP-AMD: Extending CUDA to AMD Platforms

Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C+

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high performance computing on graphics processing units: hgpu.org

Predicting the Execution Time of a kernel on a specific GPU using PTX code

Prediction of Performance and Power Consumption of GPGPU Applications

Evaluation of Rust for GPGPU high-performance computing

A Symbolic Emulator for Shuffle Synthesis on the NVIDIA PTX Code

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

Recent source codes

Performance Study of GPU-based Joins

GenVectorX: A performance-portable SYCL library for Lorentz Vectors operations

bib boi: Tools to help with LaTeX paper writing

Kernel Tuner

CuPBoP-AMD: Extending CUDA to AMD Platforms

gossip: Efficient Communication Primitives for Multi-GPU Systems

GT4Py: GridTools for Python

miniBUDE: A BUDE virtual-screening benchmark, in many programming models

AUCMEDI: a framework for Automated Classification of Medical Images

Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C+

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