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A Variant RSA Acceleration with Parallelization

Jun-jie Liu, Kang-Too Tsang, Yu-Hui Deng

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Tags: Computer science, CUDA, nVidia, RSA, Security, Tesla P100

November 28, 2021 by hgpu

HeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environments

Ji Liu, Zhihua Wu, Dianhai Yu, Yanjun Ma, Danlei Feng, Minxu Zhang, Xinxuan Wu, Xuefeng Yao, Dejing Dou

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Tags: Computer science, Deep learning, Distributed computing, Heterogeneous systems, Neural networks, nVidia, Package, Tesla V100

November 28, 2021 by hgpu

BAT: A Benchmark suite for AutoTuners

Ingunn Sund, Knut A. Kirkhorn, Jacob O. Tørring, Anne C. Elster

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Tags: Auto-Tuning, Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 980, Package, Performance, Tesla T4, Tesla V100

November 21, 2021 by hgpu

Programming Heterogeneous Systems with General and Domain-Specific Frameworks

Lukas Sommer

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce RTX 2070, OpenCL, OpenMP, Package, Python, Thesis

November 21, 2021 by hgpu

Accelerating JPEG Decompression on GPUs

André Weißenberger, Bertil Schmidt

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Tags: Compression, CUDA, Image processing, nVidia, nVidia A100

November 21, 2021 by hgpu

QGTC: Accelerating Quantized GNN via GPU Tensor Core

Yuke Wang, Boyuan Feng, Yufei Ding

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce RTX 3090, TPU

November 21, 2021 by hgpu

General purpose lattice QCD code set Bridge++ 2.0 for high performance computing

Yutaro Akahoshi, Sinya Aoki, Tatsumi Aoyama, Issaku Kanamori, Kazuyuki Kanaya, Hideo Matsufuru, Yusuke Namekawa, Hidekatsu Nemura, Yusuke Taniguchi

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Tags: GPU cluster, HEP, High Energy Physics - Lattice, Numerical simulation, nVidia, OpenACC, Package, Physics, QCD, Tesla V100

November 14, 2021 by hgpu

Performance Optimisations for Heterogeneous Managed Runtime Systems

Michail Papadimitriou

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Tags: Computer science, FPGA, Heterogeneous systems, Java, nVidia, nVidia GeForce GTX 1650, OpenCL, Performance, Thesis

November 14, 2021 by hgpu

Performance Evaluation of Python ParallelProgramming Models: Charm4Py and mpi4py

Zane Fink, Simeng Liu, Jaemin Choi, Matthias Diener, Laxmikant V. Kale

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Tags: Benchmarking, Computer science, CUDA, Distributed computing, HPC, Machine learning, MPI, nVidia, Package, Performance, Python, Tesla V100

November 14, 2021 by hgpu

Collage: Automated Integration of Deep Learning Backends

Byungsoo Jeon, Sunghyun Park, Peiyuan Liao, Sheng Xu, Tianqi Chen, Zhihao Jia

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Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce RTX 2070, Tesla V100

November 7, 2021 by hgpu

iGUARD: In-GPU Advanced Race Detection

Aditya K Kamath, Arkaprava Basu

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Tags: Computer science, CUDA, nVidia, nVidia Titan RTX, Package, Performance

November 7, 2021 by hgpu

Principles towards Real-Time Simulation of Material Point Method on Modern GPUs

Yun Fei, Yuhan Huang, Ming Gao

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Tags: Computer science, CUDA, Graphics, nVidia, nVidia DGX-1, Physics-based simulation, Tesla V100

November 7, 2021 by hgpu

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Introduction to Computer Vision

ACT: Accelerator Compiler Toolkit

Automatically Generating ML Compiler Backends from Tensor Accelerator ISA Descriptions

pytest-gpu-proof: A Simple Pytest Extension for Local GPU Code Testing

pytest-gpu-proof: Enabling Cloud-CPU Continuous Integration for GPU Code with Local GPU Attestation

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

AutoTuneBench: Trustworthy Measurement for Agent Auto-Tuning of LLM Serving Engines

nvsass-disassembler

Automated Instruction Encoding Synthesis for Modern GPU ISA Compression

PrefixBench-H100: Characterizing Prefix Reuse and Time-to-First-Token in H100 LLM Serving

EinsummableV3 - graph-based computation with tensor operations support

Every Kernel Is a Join: Automatic Multi-GPU Parallelism for AI Computations in Einsummable

MaxKernel: Intelligent TPU Kernel Engineering Agents

MaxKernel: Agentic Kernel Generation for TPUs

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high performance computing on graphics processing units: hgpu.org

A Variant RSA Acceleration with Parallelization

HeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environments

BAT: A Benchmark suite for AutoTuners

Programming Heterogeneous Systems with General and Domain-Specific Frameworks

Accelerating JPEG Decompression on GPUs

QGTC: Accelerating Quantized GNN via GPU Tensor Core

General purpose lattice QCD code set Bridge++ 2.0 for high performance computing

Performance Optimisations for Heterogeneous Managed Runtime Systems

Performance Evaluation of Python ParallelProgramming Models: Charm4Py and mpi4py

Collage: Automated Integration of Deep Learning Backends

iGUARD: In-GPU Advanced Race Detection

Principles towards Real-Time Simulation of Material Point Method on Modern GPUs

Recent source codes

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Introduction to Computer Vision

ACT: Accelerator Compiler Toolkit

pytest-gpu-proof: A Simple Pytest Extension for Local GPU Code Testing

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

AutoTuneBench: Trustworthy Measurement for Agent Auto-Tuning of LLM Serving Engines

nvsass-disassembler

PrefixBench-H100: Characterizing Prefix Reuse and Time-to-First-Token in H100 LLM Serving

EinsummableV3 - graph-based computation with tensor operations support

MaxKernel: Intelligent TPU Kernel Engineering Agents

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