high performance computing on graphics processing units: hgpu.org

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A CPU+FPGA OpenCL Heterogeneous Computing Platform for Multi-Kernel Pipeline

Yuefei Wang, Wendong Mao, Lang Feng, Jin Sha, Zhongfeng Wang

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Tags: Computer science, FPGA, Heterogeneous systems, OpenCL

June 22, 2025 by hgpu

A First Look at Bugs in LLM Inference Engines

Mugeng Liu, Siqi Zhong, Weichen Bi, Yixuan Zhang, Zhiyang Chen, Zhenpeng Chen, Xuanzhe Liu, Yun Ma

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Tags: AI, Computer science, LLM, Software Engineering

June 22, 2025 by hgpu

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

Jiaqi Lv, Xufeng He, Yanchen Liu, Xu Dai, Yang Hu, Shouyi Yin

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Tags: AI, Benchmarking, Compilers, Computer science, CUDA, Deep learning, LLM, nVidia, nVidia A100, Package, performance portability

June 15, 2025 by hgpu

GPU Acceleration of SQL Analytics on Compressed Data

Zezhou Huang, Krystian Sakowski, Hans Lehnert, Wei Cui, Carlo Curino, Matteo Interlandi, Marius Dumitru, Rathijit Sen

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Tags: Compression, Computer science, CUDA, Databases, Heterogeneous systems, nVidia, nVidia A100

June 15, 2025 by hgpu

Enabling Profile Guided Optimizations (PGO) for Graphics

Emma Jansson

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Tags: ARM, Compilers, Computer science, LLVM, Thesis, Vulkan

June 15, 2025 by hgpu

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations

Paul Fuchs, Weilong Chen, Stephan Thaler, Julija Zavadlav

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Tags: Chemistry, Computational Physics, Computer science, CUDA, Machine learning, Molecular dynamics, Neural networks, nVidia, nVidia A100, nVidia GH200, nVidia H100, Package, Physics

June 15, 2025 by hgpu

Exploring SYCL as a Portability Layer for High-Performance Computing on CPUs

Hari Abram, Nikela Papadopoulou, Miquel Pericas

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Tags: Benchmarking, Computer science, Heterogeneous systems, oneAPI, OpenCL, OpenMP, Package, SYCL

June 8, 2025 by hgpu

Acceleration as a Service (XaaS) Source Containers

Eiman Alnuaimi

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Tags: Computer science, Heterogeneous systems, HPC, Intel, Intel Data Center GPU Max 1550, LLM, MPI, nVidia, nVidia GH200, nVidia V100, Optimization, Package, performance portability, Thesis

June 8, 2025 by hgpu

MemAscend: System Memory Optimization for SSD-Offloaded LLM Fine-Tuning

Yong-Cheng Liaw, Shuo-Han Chen

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Tags: Artificial intelligence, Benchmarking, Computer science, LLM, Memory, nVidia, nVidia H100, nVidia RTX A5000

June 8, 2025 by hgpu

All You Need Is Binary Search! A Practical View on Lightweight Database Indexing on GPUs

Justus Henneberg, Felix Schuhknecht

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Tags: Computer science, CUDA, Databases, nVidia, nVidia RTX A6000, Performance

June 8, 2025 by hgpu

GPUMC: A Stateless Model Checker for GPU Weak Memory Concurrency

Soham Chakraborty, S. Krishna, Andreas Pavlogiannis, Omkar Tuppe

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Tags: Benchmarking, Computer science, CUDA, Memory, nVidia, OpenCL, Programming Languages

June 8, 2025 by hgpu

Performance of Confidential Computing GPUs

Antonio Martínez Ibarra, Julian James Stephen, Aurora González Vidal, K. R. Jayaram, Antonio Fernando Skarmeta Gómez

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Tags: Computer science, CUDA, LLM, nVidia, nVidia H100, Performance, Security

May 25, 2025 by hgpu

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Accelerating a Linear Programming Algorithm on AMD GPUs

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Towards Efficient and Practical GPU Multitasking in the Era of LLM

Bandicoot: C++ library for GPU accelerated linear algebra

Bandicoot: A Templated C++ Library for GPU Linear Algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

A CPU+FPGA OpenCL Heterogeneous Computing Platform for Multi-Kernel Pipeline

A First Look at Bugs in LLM Inference Engines

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

GPU Acceleration of SQL Analytics on Compressed Data

Enabling Profile Guided Optimizations (PGO) for Graphics

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations

Exploring SYCL as a Portability Layer for High-Performance Computing on CPUs

Acceleration as a Service (XaaS) Source Containers

MemAscend: System Memory Optimization for SSD-Offloaded LLM Fine-Tuning

All You Need Is Binary Search! A Practical View on Lightweight Database Indexing on GPUs

GPUMC: A Stateless Model Checker for GPU Weak Memory Concurrency

Performance of Confidential Computing GPUs

Recent source codes

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Bandicoot: C++ library for GPU accelerated linear algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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