high performance computing on graphics processing units: hgpu.org

hgpu.org » Heterogeneous systems

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

Zhonggen Li, Xiangyu Ke, Yifan Zhu, Yunjun Gao, Feifei Li

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia A100, Package, Performance, Prefetch

May 18, 2025 by hgpu

Efficient deep learning inference on end devices

Ehsan Aghapour

View

Download (PDF)

Source codes

Tags: Artificial intelligence, Computer science, Deep learning, Heterogeneous systems, OpenCL, Package, Thesis

May 4, 2025 by hgpu

MSCCL++: Rethinking GPU Communication Abstractions for Cutting-edge AI Applications

Aashaka Shah, Abhinav Jangda, Binyang Li, Caio Rocha, Changho Hwang, Jithin Jose, Madan Musuvathi, Olli Saarikivi, Peng Cheng, Qinghua Zhou, Roshan Dathathri, Saeed Maleki, Ziyue Yang

View

Download (PDF)

Source codes

Tags: AI, AMD Radeon Instinct MI300X, ATI, Computer science, CUDA, Heterogeneous systems, HIP, nVidia, nVidia A100, nVidia H100, Package

April 27, 2025 by hgpu

GPU-centric Communication Schemes for HPC and ML Applications

Naveen Namashivayam

View

Download (PDF)

Tags: Computer science, Deep learning, Heterogeneous systems, HPC, Machine learning, MPI, survey

April 13, 2025 by hgpu

A Power-Efficient Scheduling Approach in a Cpu-Gpu Computing System by Thread-Based Parallel Programming

Biao Hu

View

Download (PDF)

Tags: Computer science, Heterogeneous systems, nVidia GeForce RTX 3080, Optimization, Particle swarm optimization

April 13, 2025 by hgpu

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

Jiyuan Wang

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, FPGA, Heterogeneous systems, oneAPI, Package, Python, Thesis

March 30, 2025 by hgpu

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

Russel Arbore, Aaron Councilman, Xavier Routh, Ryan Ziegler, Praneet Rathi, Vikram Adve

View

Download (PDF)

Source codes

Tags: Code generation, Computer science, CUDA, Heterogeneous systems, LLVM, nVidia, nVidia GeForce RTX 2080 Ti, Package, Programming Languages, Rust

March 23, 2025 by hgpu

A Microbenchmark Framework for Performance Evaluation of OpenMP Target Offloading

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

View

Download (PDF)

Source codes

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

March 10, 2025 by hgpu

Teaching An Old Dog New Tricks: Porting Legacy Code to Heterogeneous Compute Architectures With Automated Code Translation

Nicolas Nytko, Andrew Reisner, J. David Moulton, Luke N. Olson, Matthew West

View

Download (PDF)

Tags: Code generation, Computer science, CUDA, Fortran, Heterogeneous systems, MPI, nVidia, nVidia A100, OpenCL

February 16, 2025 by hgpu

Towards autonomous resource management: Deep learning prediction of CPU-GPU load balancing

Inigo Gabirondo Lopez

View

Download (PDF)

Tags: AMD Radeon HD 7970, Artificial intelligence, ATI, Computer science, Deep learning, Heterogeneous systems, load balancing, nVidia, nVidia GeForce GTX 970, OpenCL

February 10, 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

View

Download (PDF)

Source codes

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

February 10, 2025 by hgpu

CPU-GPU co-execution through the exploitation of hybrid technologies via SYCL

Nozal Raúl, Jose Luis Bosque

View

Download (PDF)

Tags: Computer science, CUDA, Heterogeneous systems, Hybrid computing, LLVM, load balancing, nVidia, nVidia GeForce GT 1030, oneAPI, OpenCL, performance portability, SYCL

February 3, 2025 by hgpu

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

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

Can Large Language Models Predict Parallel Code Performance?

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

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

Efficient deep learning inference on end devices

MSCCL++: Rethinking GPU Communication Abstractions for Cutting-edge AI Applications

GPU-centric Communication Schemes for HPC and ML Applications

A Power-Efficient Scheduling Approach in a Cpu-Gpu Computing System by Thread-Based Parallel Programming

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

A Microbenchmark Framework for Performance Evaluation of OpenMP Target Offloading

Teaching An Old Dog New Tricks: Porting Legacy Code to Heterogeneous Compute Architectures With Automated Code Translation

Towards autonomous resource management: Deep learning prediction of CPU-GPU load balancing

CPU-GPU co-execution through the exploitation of hybrid technologies via SYCL

Recent source codes

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

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

Most viewed papers (last 30 days)