high performance computing on graphics processing units: hgpu.org

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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

PyGraph: Robust Compiler Support for CUDA Graphs in PyTorch

Abhishek Ghosh, Ajay Nayak, Ashish Panwar, Arkaprava Basu

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Tags: Benchmarking, Computer science, CUDA, Machine learning, nVidia, nVidia RTX A6000, PyTorch

March 30, 2025 by hgpu

Analyzing Modern NVIDIA GPU cores

Rodrigo Huerta, Mojtaba Abaie Shoushtary, José-Lorenzo Cruz, Antonio González

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Tags: Computer science, Hardware Architecture, nVidia, nVidia RTX A6000

March 30, 2025 by hgpu

A Microbenchmark Framework for Performance Evaluation of OpenMP Target Offloading

Mohammad Atif, Tianle Wang, Zhihua Dong, Charles Leggett, Meifeng Lin

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Tags: Benchmarking, Computer science, Heterogeneous systems, nVidia A100, nVidia RTX A6000, nVidia V100, OpenMP, Package

March 10, 2025 by hgpu

CRIUgpu: Transparent Checkpointing of GPU-Accelerated Workloads

Radostin Stoyanov, Viktória Spišaková, Jesus Ramos, Steven Gurfinkel, Andrei Vagin, Adrian Reber, Wesley Armour, Rodrigo Bruno

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Tags: AMD Radeon Instinct MI210, ATI, Computer science, CUDA, Deep learning, nVidia, nVidia A100, nVidia H100, nVidia RTX A6000, Package, ROCm

March 3, 2025 by hgpu

Demystifying Cost-Efficiency in LLM Serving over Heterogeneous GPUs

Youhe Jiang, Fangcheng Fu, Xiaozhe Yao, Guoliang He, Xupeng Miao, Ana Klimovic, Bin Cui, Binhang Yuan, Eiko Yoneki

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Tags: Benchmarking, Cloud, Computer science, Heterogeneous systems, LLM, nVidia, nVidia GeForce RTX 4090, nVidia H100, nVidia RTX A6000, Package

February 10, 2025 by hgpu

Profiling Apple Silicon Performance for ML Training

Dahua Feng, Zhiming Xu, Rongxiang Wang, Felix Xiaozhu Lin

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Tags: AI, Apple M2 Max, Apple M2 Pro, Apple M2 Ultra, Computer science, CUDA, Linear Algebra, LLM, Machine learning, nVidia, nVidia GeForce RTX 4090, nVidia GeFroce RTX 2080 Ti, nVidia Quadro RTX 4000, nVidia RTX A6000, Performance, PyTorch

February 3, 2025 by hgpu

LeetDecoding: A PyTorch Library for Exponentially Decaying Causal Linear Attention with CUDA Implementations

Jiaping Wang, Simiao Zhang, Qiao-Chu He, Yifan Chen

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Tags: Benchmarking, Computer science, CUDA, LLM, Machine learning, nVidia, nVidia A100, nVidia RTX A6000, Package, Python, PyTorch

January 13, 2025 by hgpu

Efficient Configuration of Heterogeneous Resources and Task Scheduling Strategies in Deep Learning Auto-Tuning Systems

Pao-Yi Ken, Chao-Chin Wu

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Tags: AI, Artificial intelligence, Computer science, Deep learning, Machine learning, nVidia, nVidia RTX A6000, Task scheduling

October 20, 2024 by hgpu

Spyx: A Library for Just-In-Time Compiled Optimization of Spiking Neural Networks

Kade M. Heckel, Thomas Nowotny

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Tags: Artificial intelligence, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia RTX A6000, Package, Python

March 3, 2024 by hgpu

QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference

Taesu Kim, Jongho Lee, Daehyun Ahn, Sarang Kim, Jiwoong Choi, Minkyu Kim, Hyungjun Kim

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Tags: Computer science, CUDA, Deep learning, Machine learning, Matrix multiplication, Mixed precision, nVidia, nVidia A100, nVidia GeForce RTX 4090, nVidia RTX A6000, Package

February 18, 2024 by hgpu

Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C+

Bin Lei, Caiwen Ding, Le Chen, Pei-Hung Lin, Chunhua Liao

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Tags: Benchmarking, Computer science, CUDA, Fortran, Machine learning, nVidia, nVidia RTX A6000, OpenMP, Package

November 19, 2023 by hgpu

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Efficient deep learning inference on end devices

Ouroboros: Virtualized Queues for dynamic memory management

Dynamic Memory Management on GPUs with SYCL

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high performance computing on graphics processing units: hgpu.org

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

PyGraph: Robust Compiler Support for CUDA Graphs in PyTorch

Analyzing Modern NVIDIA GPU cores

A Microbenchmark Framework for Performance Evaluation of OpenMP Target Offloading

CRIUgpu: Transparent Checkpointing of GPU-Accelerated Workloads

Profiling Apple Silicon Performance for ML Training

LeetDecoding: A PyTorch Library for Exponentially Decaying Causal Linear Attention with CUDA Implementations

Efficient Configuration of Heterogeneous Resources and Task Scheduling Strategies in Deep Learning Auto-Tuning Systems

Spyx: A Library for Just-In-Time Compiled Optimization of Spiking Neural Networks

QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference

Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C+

Recent source codes

XaaS containers

microSYCL: SYCL micro-benchmarks repository

SYCL Container

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Cluser of smartphones for edge computing application using TensorFlow

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

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