high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

Novel Parallel Approaches to Efficiently Solve Spatial Problems on Heterogeneous CPU-GPU Systems

Andrés Jesús Sánchez Fernández

View

Tags: Algorithms, Computer science, Databases, Heterogeneous systems, nVidia, nVidia GeForce GT 1030, nVidia GeForce GTX 1050 Ti, nVidia GeForce GTX 980, Thesis

July 3, 2022 by hgpu

Evaluation of Intel’s DPC++ Compatibility Tool in heterogeneous computing

Germán Castaño, Youssef Faqir-Rhazoui, Carlos García, Manuel Prieto-Matías

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, Package, Performance, SYCL

July 3, 2022 by hgpu

Optimizing the Performance of Parallel and Concurrent Applications Based on Asynchronous Many-Task Runtimes

Weile Wei

View

Download (PDF)

Source codes

Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, Optimization, Package, QMC, Tesla V100, Thesis

July 3, 2022 by hgpu

Tensor Computation Based on Heterogeneous Memory

Jiawen Liu

View

Download (PDF)

Tags: Computer science, Heterogeneous systems, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, OpenCL, Prefetch, Thesis

July 3, 2022 by hgpu

TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s

Felix Chern, Blake Hechtman, Andy Davis, Ruiqi Guo, David Majnemer, Sanjiv Kumar

View

Download (PDF)

Tags: Algorithms, Computer science, Nearest neighbour, nVidia, nVidia A100, Tesla V100, TPU

July 3, 2022 by hgpu

An experimental study of group-by and aggregation on CPU-GPU processors

Hua Luan, Lei Chang

View

Download (PDF)

Tags: Computer science, Databases, Hashing, OpenCL

June 26, 2022 by hgpu

MapReduce for Counting Word Frequencies with MPI and GPUs

Nithin Kavi

View

Download (PDF)

Source codes

Tags: Algorithms, Computer science, CUDA, Julia, MapReduce, MPI, NLP, nVidia, Optimization, Package, Tesla V100

June 19, 2022 by hgpu

PILC: Practical Image Lossless Compression with an End-to-end GPU Oriented Neural Framework

Ning Kang, Shanzhao Qiu, Shifeng Zhang, Zhenguo Li, Shutao Xia

View

Download (PDF)

Tags: Algorithms, Compression, Image processing, nVidia, Tesla V100

June 19, 2022 by hgpu

Securing GPU via Region-based Bounds Checking

Jaewon Lee, Yonghae Kim, Jiashen Cao, Euna Kim, Jaekyu Lee, Hyesoon Kim

View

Download (PDF)

Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce RTX 2080, OpenCL, Security

June 19, 2022 by hgpu

CuPBoP: CUDA for Parallelized and Broad-range Processors

Ruobing Han, Jun Chen, Bhanu Garg, Jeffrey Young, Jaewoong Sim, Hyesoon Kim

View

Download (PDF)

Tags: Benchmarking, Compilers, Computer science, CUDA, Heterogeneous systems, HIP, MPI, nVidia, nVidia GeForce GTX 1660 Ti, OpenMP

June 19, 2022 by hgpu

On the Compilation Performance of Current SYCL Implementations

Peter Thoman, Facundo Molina Heredia, Thomas Fahringer

View

Download (PDF)

Source codes

Tags: Code generation, Compilers, Computer science, Heterogeneous systems, OpenCL, Package, Performance, SYCL

June 12, 2022 by hgpu

Modeling GPU Dynamic Parallelism for Self Similar Density Workloads

Felipe A. Quezada, Cristóbal A. Navarro, Miguel Romero, Cristhian Aguilera

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce Titan V, nVidia Jetson NX, nVidia Titan RTX, Performance

June 12, 2022 by hgpu

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

CONCUR: a benchmark designed to evaluate multithreaded Java code generated by LLMs

CONCUR: Benchmarking LLMs for Concurrent Code Generation

HIPRT: Ray Tracing using HIP

Ray Tracing using HIP

MXFP4 Training Support Codebase

Practical FP4 Training for Large-Scale MoE Models on Hopper GPUs

CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation

CUDABench: Benchmarking LLMs for Text-to-CUDA Generation

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Programming

Novel Parallel Approaches to Efficiently Solve Spatial Problems on Heterogeneous CPU-GPU Systems

Evaluation of Intel’s DPC++ Compatibility Tool in heterogeneous computing

Optimizing the Performance of Parallel and Concurrent Applications Based on Asynchronous Many-Task Runtimes

Tensor Computation Based on Heterogeneous Memory

TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s

An experimental study of group-by and aggregation on CPU-GPU processors

MapReduce for Counting Word Frequencies with MPI and GPUs

PILC: Practical Image Lossless Compression with an End-to-end GPU Oriented Neural Framework

Securing GPU via Region-based Bounds Checking

CuPBoP: CUDA for Parallelized and Broad-range Processors

On the Compilation Performance of Current SYCL Implementations

Modeling GPU Dynamic Parallelism for Self Similar Density Workloads

Recent source codes

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

CONCUR: a benchmark designed to evaluate multithreaded Java code generated by LLMs

HIPRT: Ray Tracing using HIP

MXFP4 Training Support Codebase

CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation

CUDABench: Benchmarking LLMs for Text-to-CUDA Generation

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

Most viewed papers (last 30 days)