high performance computing on graphics processing units: hgpu.org

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Tensor Computation Based on Heterogeneous Memory

Jiawen Liu

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Tags: Computer science, Heterogeneous systems, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, OpenCL, Prefetch, Thesis

July 3, 2022 by hgpu

TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s

Felix Chern, Blake Hechtman, Andy Davis, Ruiqi Guo, David Majnemer, Sanjiv Kumar

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Tags: Algorithms, Computer science, Nearest neighbour, nVidia, nVidia A100, Tesla V100, TPU

July 3, 2022 by hgpu

SnuHPL: high performance LINPACK for heterogeneous GPUs

Jinpyo Kim, Hyungdal Kwon, Jintaek Kang, Jihwan Park, Seungwook Lee, Jaejin Lee

Tags: Benchmarking, Computer science, nVidia, nVidia A100

June 26, 2022 by hgpu

tntorch: Tensor Network Learning with PyTorch

Mikhail Usvyatsov, Rafael Ballester-Ripoll, Konrad Schindler

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Tags: Computer science, Deep learning, nVidia, nVidia GeForce RTX 3090, Package, Python, PyTorch

June 26, 2022 by hgpu

PILC: Practical Image Lossless Compression with an End-to-end GPU Oriented Neural Framework

Ning Kang, Shanzhao Qiu, Shifeng Zhang, Zhenguo Li, Shutao Xia

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Tags: Algorithms, Compression, Image processing, nVidia, Tesla V100

June 19, 2022 by hgpu

Securing GPU via Region-based Bounds Checking

Jaewon Lee, Yonghae Kim, Jiashen Cao, Euna Kim, Jaekyu Lee, Hyesoon Kim

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce RTX 2080, OpenCL, Security

June 19, 2022 by hgpu

MapReduce for Counting Word Frequencies with MPI and GPUs

Nithin Kavi

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Tags: Algorithms, Computer science, CUDA, Julia, MapReduce, MPI, NLP, nVidia, Optimization, Package, Tesla V100

June 19, 2022 by hgpu

CuPBoP: CUDA for Parallelized and Broad-range Processors

Ruobing Han, Jun Chen, Bhanu Garg, Jeffrey Young, Jaewoong Sim, Hyesoon Kim

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Tags: Benchmarking, Compilers, Computer science, CUDA, Heterogeneous systems, HIP, MPI, nVidia, nVidia GeForce GTX 1660 Ti, OpenMP

June 19, 2022 by hgpu

Modeling GPU Dynamic Parallelism for Self Similar Density Workloads

Felipe A. Quezada, Cristóbal A. Navarro, Miguel Romero, Cristhian Aguilera

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Tags: Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce Titan V, nVidia Jetson NX, nVidia Titan RTX, Performance

June 12, 2022 by hgpu

Seamless GPU acceleration for C++ based physics with the Metal Shading Language on Apple’s M series unified chips

Lars Gebraad, Andreas Fichtner

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Tags: Benchmarking, Computer science, Finite difference, nVidia, Package, Physics, Wave equation

June 12, 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

Onesweep: A Faster Least Significant Digit Radix Sort for GPUs

Andy Adinets, Duane Merrill

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia A100, Package, Performance, Sorting

June 12, 2022 by hgpu

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

A Performance Portable Matrix Free Dense MTTKRP in GenTen

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

ConCuR: Conciseness Makes State-of-the-Art Kernel Generation

VibeCodeHPC - Multi Agentic Vibe Coding for HPC

VibeCodeHPC: An Agent-Based Iterative Prompting Auto-Tuner for HPC Code Generation Using LLMs

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

exa-AMD: Exascale Accelerated Materials Discovery

exa-AMD: An Exascale-Ready Framework for Accelerating the Discovery and Design of Functional Materials

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high performance computing on graphics processing units: hgpu.org

Tensor Computation Based on Heterogeneous Memory

TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s

SnuHPL: high performance LINPACK for heterogeneous GPUs

tntorch: Tensor Network Learning with PyTorch

PILC: Practical Image Lossless Compression with an End-to-end GPU Oriented Neural Framework

Securing GPU via Region-based Bounds Checking

MapReduce for Counting Word Frequencies with MPI and GPUs

CuPBoP: CUDA for Parallelized and Broad-range Processors

Modeling GPU Dynamic Parallelism for Self Similar Density Workloads

Seamless GPU acceleration for C++ based physics with the Metal Shading Language on Apple’s M series unified chips

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

Onesweep: A Faster Least Significant Digit Radix Sort for GPUs

Recent source codes

Kernel Library for LLM Serving

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

VibeCodeHPC - Multi Agentic Vibe Coding for HPC

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

exa-AMD: Exascale Accelerated Materials Discovery

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