high performance computing on graphics processing units: hgpu.org

Programming

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GC3: An Optimizing Compiler for GPU Collective Communication

Meghan Cowan, Saeed Maleki, Madanlal Musuvathi, Olli Saarikivi, Yifan Xiong

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Tags: Algorithms, Compilers, Computer science, CUDA, Machine learning, nVidia, nVidia A100

February 6, 2022 by hgpu

Teaching Parallel Programming in Containers: Virtualization of a Heterogeneous Local Infrastructure

Naylor Garcia Bachiega

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Tags: Computer science, CUDA, Heterogeneous systems, HPC, nVidia, nVidia GeForce GTX 650, Package, Thesis, Virtualization

January 30, 2022 by hgpu

Optimizing Huffman Decoding for Error-Bounded Lossy Compression on GPUs

Cody Rivera, Sheng Di, Jiannan Tian, Xiaodong Yu, Dingwen Tao, Franck Cappello

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, Package, Tesla V100

January 30, 2022 by hgpu

Bit-GraphBLAS: Bit-Level Optimizations of Matrix-Centric Graph Processing on GPU

Jou-An Chen, Hsin-Hsuan Sung, Nathan Tallent, Kevin Barker, Xipeng Shen, Ang Li

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Tags: BLAS, Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX Titan V, Package, Sparse

January 30, 2022 by hgpu

Multi-hetero Acceleration by GPU and FPGA for Astrophysics Simulation on oneAPI Environment

Ryuta Kashino, Ryohei Kobayashi, Norihisa Fujita, Taisuke Boku

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Tags: Astrophysics, CUDA, FPGA, Heterogeneous systems, nVidia, OpenCL, Physics, SYCL

January 23, 2022 by hgpu

A tool set for random number generation on GPUs in R

Ruoyong Xu, Patrick Brown, Pierre L'Ecuyer

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Tags: Computer science, OpenCL, Package, Pseudo-random number generators, R

January 23, 2022 by hgpu

Reusing Auto-Schedules for Efficient DNN Compilation

Perry Gibson, José Cano

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Tags: Algorithms, Computer science, Machine learning, Neural networks, Performance, Programming Languages

January 23, 2022 by hgpu

NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics

Raimondas Galvelis, Alejandro Varela-Rial, Stefan Doerr, Roberto Fino, Peter Eastman, Thomas E. Markland, John D. Chodera, Gianni De Fabritiis

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Tags: Biology, Chemistry, CUDA, GeForce RTX 2080 Ti, Machine learning, Molecular dynamics, Molecular simulation, Neural networks, nVidia, Package

January 23, 2022 by hgpu

Building a Performance Model for Deep Learning Recommendation Model Training on GPUs

Zhongyi Lin, Louis Feng, Ehsan K. Ardestani, Jaewon Lee, John Lundell, Changkyu Kim, Arun Kejariwal, John D. Owens

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Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX Titan XP, Package, Performance, Tesla P100, Tesla V100

January 23, 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

Dopia: Online Parallelism Management for Integrated CPU/GPU Architectures

Younghyun Cho, Jiyeon Park, Florian Negele, Changyeon Jo, Thomas R. Gross, Bernhard Egger

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Tags: Code generation, Computer science, Machine learning, OpenCL

January 16, 2022 by hgpu

Fancier: A Unified Framework for Java, C, and OpenCL Integration

Sergio Afonso, Francisco Almeida

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Tags: Computer science, Image processing, Java, OpenACC, OpenCL, Package, Performance

January 16, 2022 by hgpu

CUDAnalyst (CUDA + Analyst)

Towards Feedback-to-Plan Decisions for Self-Evolving LLM Agents in CUDA Kernel Generation

CodegenBench

CodegenBench: Can LLMs Write Efficient Code Across Architectures?

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Agentic Code Optimization via Compiler-LLM Cooperation

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

GC3: An Optimizing Compiler for GPU Collective Communication

Teaching Parallel Programming in Containers: Virtualization of a Heterogeneous Local Infrastructure

Optimizing Huffman Decoding for Error-Bounded Lossy Compression on GPUs

Bit-GraphBLAS: Bit-Level Optimizations of Matrix-Centric Graph Processing on GPU

Multi-hetero Acceleration by GPU and FPGA for Astrophysics Simulation on oneAPI Environment

A tool set for random number generation on GPUs in R

Reusing Auto-Schedules for Efficient DNN Compilation

NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics

Building a Performance Model for Deep Learning Recommendation Model Training on GPUs

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

Dopia: Online Parallelism Management for Integrated CPU/GPU Architectures

Fancier: A Unified Framework for Java, C, and OpenCL Integration

Recent source codes

CUDAnalyst (CUDA + Analyst)

CodegenBench

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Device Virtual Machine (DVM)

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Agentic Code Optimization via Compiler-LLM Cooperation

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