high performance computing on graphics processing units: hgpu.org

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

Tian Lan, Sunil Srinivasa, Stephan Zheng

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Tags: Computer science, CUDA, Machine learning, nVidia, Package, Python, Tesal V100

September 5, 2021 by hgpu

A Survey of Big Data, High Performance Computing, and Machine Learning Benchmarks

Nina Ihde, Paula Marten, Ahmed Eleliemy, Gabrielle Poerwawinata, Pedro Silva, Ilin Tolovski, Florina M. Ciorba, Tilmann Rabl

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Tags: Benchmarking, Computer science, HPC, Machine learning, survey

August 29, 2021 by hgpu

DNN is not all you need: Parallelizing Non-Neural ML Algorithms on Ultra-Low-Power IoT Processors

Enrico Tabanelli, Giuseppe Tagliavini, Luca Benini

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Tags: Computer science, FPGA, Machine learning, Neural networks, nVidia, Package, SoC, Tesla A100

July 25, 2021 by hgpu

GPTPU: Accelerating Applications using Edge Tensor Processing Units

Kuan-Chieh Hsu, Hung-Wei Tseng

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, Machine learning, Neural networks, nVidia, OpenCL, TPU

July 18, 2021 by hgpu

KAISA: An Adaptive Second-order Optimizer Framework for Deep Neural Networks

J. Gregory Pauloski, Qi Huang, Lei Huang, Shivaram Venkataraman, Kyle Chard, Ian Foster, Zhao Zhang

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Tags: Computer science, CUDA, Machine learning, Neural networks, nVidia, nVidia A100, nVidia DGX-A100, Package, Tesla V100

July 11, 2021 by hgpu

Opening the Black Box: Performance Estimation during Code Generation for GPUs

Dominik Ernst, Georg Hager, Markus Holzer, Matthias Knorr, Gerhard Wellein

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Tags: Code generation, Computer science, CUDA, Lattice Boltzmann model, Machine learning, nVidia, Performance, Stencil computation, Tesla V100

July 11, 2021 by hgpu

Optimization of Heterogeneous Parallel Computing Systems using Machine Learning

Devi Abhiseshu Adurti, Mohit Battu

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Tags: Computer science, CUDA, Heterogeneous systems, Machine learning, Matrix multiplication, nVidia, nVidia GeForce GTX 1050 Ti, Thesis

July 4, 2021 by hgpu

HALF: Holistic Auto Machine Learning for FPGAs

Jonas Ney, Dominik Loroch, Vladimir Rybalkin, Nico Weber, Jens Krüger, Norbert Wehn

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Tags: Computer science, Design space exploration, FPGA, HLS, Machine learning, Neural networks

July 4, 2021 by hgpu

Lettuce: PyTorch-based Lattice Boltzmann Framework

Mario Christopher Bedrunka, Dominik Wilde, Martin Kliemank, Dirk Reith, Holger Foysi, Andreas Krämer

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Tags: Algorithms, Computational Physics, CUDA, Deep learning, Fluid dynamics, Lattice Boltzmann model, Machine learning, nVidia, nVidia GeForce RTX 2070, Package, Physics, Tesla V100

June 27, 2021 by hgpu

StreamBrain: An HPC Framework for Brain-like Neural Networks on CPUs, GPUs and FPGAs

Artur Podobas, Martin Svedin, Steven W. D. Chien, Ivy B. Peng, Naresh Balaji Ravichandran, Pawel Herman, Anders Lansner, Stefano Markidis

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Tags: Bayesian, Computer science, CUDA, Deep learning, FPGA, HPC, Machine learning, MPI, nVidia, OpenMPI, Package, Tesla A100, Tesla V100

June 20, 2021 by hgpu

Optimization of Heterogeneous Systems with AI Planning Heuristics and Machine Learning: A Performance and Energy Aware Approach

Suejb Memeti, Sabri Pllana

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Tags: Artificial intelligence, Computer science, Heterogeneous systems, Intel Xeon Phi, Machine learning

June 6, 2021 by hgpu

TENSILE: A Tensor granularity dynamic GPU memory scheduler method towards multiple dynamic workloads system

Kaixin Zhang, Hongzhi Wang, Tongxin Li, Han Hu, Jiye Qiu, Songling Zou

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Tags: Computer science, CUDA, Databases, Deep learning, Machine learning, nVidia, nVidia GeForce RTX 2080 Ti, Task scheduling

May 30, 2021 by hgpu

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

LC Framework

Characterizing the Performance of Parallel Data-Compression Algorithms across Compilers and GPUs

pplx-garden: Perplexity open source garden for inference technology

RDMA Point-to-Point Communication for LLM Systems

See all packages

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high performance computing on graphics processing units: hgpu.org

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

A Survey of Big Data, High Performance Computing, and Machine Learning Benchmarks

DNN is not all you need: Parallelizing Non-Neural ML Algorithms on Ultra-Low-Power IoT Processors

GPTPU: Accelerating Applications using Edge Tensor Processing Units

KAISA: An Adaptive Second-order Optimizer Framework for Deep Neural Networks

Opening the Black Box: Performance Estimation during Code Generation for GPUs

Optimization of Heterogeneous Parallel Computing Systems using Machine Learning

HALF: Holistic Auto Machine Learning for FPGAs

Lettuce: PyTorch-based Lattice Boltzmann Framework

StreamBrain: An HPC Framework for Brain-like Neural Networks on CPUs, GPUs and FPGAs

Optimization of Heterogeneous Systems with AI Planning Heuristics and Machine Learning: A Performance and Energy Aware Approach

TENSILE: A Tensor granularity dynamic GPU memory scheduler method towards multiple dynamic workloads system

Recent source codes

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

LC Framework

pplx-garden: Perplexity open source garden for inference technology

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