high performance computing on graphics processing units: hgpu.org

hgpu.org » Programming techniques

Automatic program analysis for data parallel kernels

Calin Juravle

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, Optimization, Performance, Programming techniques, Thesis

October 20, 2011 by hgpu

An Efficient Stream Buffer Mechanism for Dataflow Execution on Heterogeneous Platforms with GPUs

Ana Balevic, Bart Kienhuis

View

Download (PDF)

Tags: Computer science, CUDA, Data parallelism, Heterogeneous systems, nVidia, Programming techniques, Tesla C2050

October 19, 2011 by hgpu

A Code Transformation Framework for Scientific Applications on Structured Grids

Pei-Hung Lin, Jagan Jayaraj, Paul Woodward, Pen-chung Yew

View

Download (PDF)

Tags: Compilers, Computer science, Fluid dynamics, Optimization, Performance, Programming techniques

October 19, 2011 by hgpu

Enabling Traceability in an MDE Approach to Improve Performance of GPU Applications

Antonio Wendellde O. Rodrigues, Vincent Aranega, Anne Etien, Frederic Guyomarc'h, Jean-Luc Dekeyser

View

Download (PDF)

Tags: Algorithms, Computer science, nVidia, OpenCL, Performance, Programming techniques, Tesla S1070

October 18, 2011 by hgpu

Data-Driven Programming Abstractions and Optimization for Multi-Core Platforms

Rebecca L. Collins

View

Download (PDF)

Tags: Algorithms, Cell processor, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, Optimization, Programming techniques, Thesis

October 17, 2011 by hgpu

Dynamic Fine-Grain Scheduling of Pipeline Parallelism

Daniel Sanchez, David Lo, Richard M. Yoo, Jeremy Sugerman, Christos Kozyrakis

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, Performance, Programming techniques

October 17, 2011 by hgpu

Programming with Explicit Dependencies. A Framework for Portable Parallel Programming

Eva Burrows

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 9600 GT, nVidia GeForce GTX 250, Programming techniques, Sorting, Thesis

October 17, 2011 by hgpu

GPU-to-CPU callbacks

Jeff A. Stuart, Michael Cox, John D. Owens

View

Download (PDF)

Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 280, Performance, Programming techniques

October 16, 2011 by hgpu

Physis: An Implicitly Parallel Programming Model for Stencil Computations on Large-Scale GPU-Accelerated Supercomputers

Naoya Maruyama, Tatsuo Nomura, Kento Sato, Satoshi Matsuoka

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, MPI, nVidia, Optimization, Package, Performance, Programming Languages, Programming techniques, Tesla M2050

October 15, 2011 by hgpu

Accelerating code on multi-cores with FastFlow

Marco Aldinucci, Marco Danelutto, Peter Kilpatrick, Massimiliano Meneghin, Massimo Torquati

View

Download (PDF)

Source codes

Tags: Computer science, Memory, Package, Performance, Programming techniques

October 15, 2011 by hgpu

An Analysis of Programmer Productivity versus Performance for High Level Data Parallel Programming

Alex Cole, Alistair McEwan, Satnam Singh

View

Download (PDF)

Tags: Computer science, CUDA, FPGA, nVidia, nVidia GeForce GTX 460, Programming techniques

October 14, 2011 by hgpu

Towards scalar synchronization in SIMT architectures

Arun Ramamurthy

View

Download (PDF)

Tags: Benchmarking, Computer science, CUDA, nVidia, OpenCL, Performance, Programming techniques, Thesis

October 14, 2011 by hgpu

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

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

Generating Literature-Driven Scientific Theories at Scale

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Automatic program analysis for data parallel kernels

An Efficient Stream Buffer Mechanism for Dataflow Execution on Heterogeneous Platforms with GPUs

A Code Transformation Framework for Scientific Applications on Structured Grids

Enabling Traceability in an MDE Approach to Improve Performance of GPU Applications

Data-Driven Programming Abstractions and Optimization for Multi-Core Platforms

Dynamic Fine-Grain Scheduling of Pipeline Parallelism

Programming with Explicit Dependencies. A Framework for Portable Parallel Programming

GPU-to-CPU callbacks

Physis: An Implicitly Parallel Programming Model for Stencil Computations on Large-Scale GPU-Accelerated Supercomputers

Accelerating code on multi-cores with FastFlow

An Analysis of Programmer Productivity versus Performance for High Level Data Parallel Programming

Towards scalar synchronization in SIMT architectures

Recent source codes

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

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Vortex-Optimized Light-weight Toolchain (VOLT)

SciDef: Automated Definition Extraction from Scientific Literature

bioagent-bench: Benchmark for evaluating LLM agents in bioinformatics

Benchmark suite for LLM inference on NVIDIA consumer GPUs

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

Most viewed papers (last 30 days)