high performance computing on graphics processing units: hgpu.org

hgpu.org » Heterogeneous systems

A High-Throughput GPU Framework for Adaptive Lossless Compression of Floating-Point Data

Zheng Li, Weiyan Wang, Ruiyuan Li, Chao Chen, Xianlei Long, Linjiang Zheng, Quanqing Xu, Chuanhui Yang

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Tags: Algorithms, Compression, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce RTX 5080, Package

November 16, 2025 by hgpu

Enhancing Transformer Performance and Portability through Auto-tuning Frameworks

Patricia Siwinska,Jie Lei,Adrian Castello,Pedro Alonso-Jord́a,Enrique S. Quintana-Orti

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Tags: Auto-Tuning, Computer science, CUDA, Deep learning, Heterogeneous systems, LLM, nVidia, nVidia A100, Package, performance portability

November 2, 2025 by hgpu

Compiler and Runtime Systems for Generative AI Models

Zihao Ye

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce RTX 3070, nVidia H100, Package, Tesla V100

October 19, 2025 by hgpu

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

M. D. Lepinzan, G. Lacopo, D. Goz, G. Taffoni, P. Monaco, P. J. Elahi, U. Varetto, M. Cytowski

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Tags: AMD Radeon Instinct MI250X, Astrophysics, ATI, Benchmarking, Heterogeneous systems, HIP, HPC, Instrumentation and Methods for Astrophysics, nVidia, nVidia A100, OpenMP, Package

October 12, 2025 by hgpu

Scalable Engine and the Performance of Different LLM Models in a SLURM based HPC architecture

Anderson de Lima Luiz, Shubham Vijay Kurlekar, Munir Georges

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Tags: Computer science, Heterogeneous systems, HPC, LLM, nVidia, nVidia A100

August 31, 2025 by hgpu

ConTraPh: Contrastive Learning for Parallelization and Performance Optimization

Quazi Ishtiaque Mahmud, Ali TehraniJamsaz, Nesreen K. Ahmed, Theodore L. Willke, Ali Jannesari

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Tags: Code generation, Computer science, Heterogeneous systems, HPC, LLM, nVidia, OpenACC, OpenCL, OpenMP, Tesla V100

August 10, 2025 by hgpu

OpenDwarfs 2025: Modernizing the OpenDwarfs Benchmark Suite for Heterogeneous Computing

Juan José Ropero, Manuel de Castro, Diego R. Llanos

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Tags: AMD FirePro W7800, ATI, Benchmarking, Computer science, Heterogeneous systems, nVidia, nVidia RTX 4500 Ada, OpenCL, Package

August 3, 2025 by hgpu

Specx: a C++ task-based runtime system for heterogeneous distributed architectures

Paul Cardosi, Bérenger Bramas

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Tags: Computer science, CUDA, Heterogeneous systems, HIP, nVidia, nVidia A100, nVidia P100, nVidia Quadro RTX 8000, nVidia V100, Package, Task scheduling

July 20, 2025 by hgpu

Libra: Synergizing CUDA and Tensor Cores for High-Performance Sparse Matrix Multiplication

Jinliang Shi, Shigang Li, Youxuan Xu, Xueying Wang, Rongtian Fu, Zhi Ma, Tong Wu

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce RTX 4090, nVidia H100, Package, Sparse matrix

July 6, 2025 by hgpu

LiteGD: Lightweight and dynamic GPU Dispatching for Large-scale Heterogeneous Clusters

Kunming Zhang, Hanlong Liao, Guoming Tang

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Tags: Computer science, GPU cluster, Heterogeneous systems, Machine learning, nVidia, nVidia A800, nVidia GeForce RTX 4090, nVidia H100, nVidia RTX A6000, nVidia V100

June 22, 2025 by hgpu

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

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

Agentic Code Optimization via Compiler-LLM Cooperation

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

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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high performance computing on graphics processing units: hgpu.org

A High-Throughput GPU Framework for Adaptive Lossless Compression of Floating-Point Data

Enhancing Transformer Performance and Portability through Auto-tuning Frameworks

Compiler and Runtime Systems for Generative AI Models

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

Scalable Engine and the Performance of Different LLM Models in a SLURM based HPC architecture

ConTraPh: Contrastive Learning for Parallelization and Performance Optimization

OpenDwarfs 2025: Modernizing the OpenDwarfs Benchmark Suite for Heterogeneous Computing

Specx: a C++ task-based runtime system for heterogeneous distributed architectures

Libra: Synergizing CUDA and Tensor Cores for High-Performance Sparse Matrix Multiplication

LiteGD: Lightweight and dynamic GPU Dispatching for Large-scale Heterogeneous Clusters

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

GPU Acceleration of SQL Analytics on Compressed Data

Recent source codes

Agentic Code Optimization via Compiler-LLM Cooperation

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

Device Virtual Machine (DVM)

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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