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Accelerating Stochastic Simulations on GPUs Using OpenCL

Pilsung Kang

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Tags: Heterogeneous systems, nVidia, nVidia GeForce GTX 1080 Ti, OpenCL, Physics, Stochastic simulation

November 10, 2019 by hgpu

Code Optimization on GPUs

Changwan Hong

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Tags: Chemistry, Code generation, Computational chemistry, Computer science, CUDA, Intel Xeon Phi, Matrix multiplication, nVidia, Optimization, Sparse matrix, Tesla P100, Thesis

November 3, 2019 by hgpu

JSDoop and TensorFlow.js: Volunteer Distributed Web Browser-Based Neural Network Training

José Á. Morell, Andrés Camero, Enrique Alba

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Tags: Computer science, Deep learning, Distributed computing, Package, TensorFlow

November 3, 2019 by hgpu

In-memory database acceleration on FPGAs: a survey

Jian Fang, Yvo T. B. Mulder, Jan Hidders, Jinho Lee, H. Peter Hofstee

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Tags: Computer science, Databases, FPGA, HLS, OpenCL, survey

November 3, 2019 by hgpu

Implementing and evaluating an heterogeneous, scalable, tridiagonal linear system solver with OpenCL to target FPGAs, GPUs, and CPUs

Hamish J. Macintosh, Jasmine E. Banks, Neil A. Kelson

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Tags: Computer science, FPGA, Linear Algebra, nVidia, nVidia Quadro M 4000, OpenCL

November 3, 2019 by hgpu

Research on OpenCL optimization for FPGA deep learning application

Shuo Zhang, Yanxia Wu, Chaoguang Men, Hongtao He, Kai Liang

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Tags: Computer science, Deep learning, FPGA, OpenCL, Package

November 3, 2019 by hgpu

PyTorchPipe: a framework for rapid prototyping of pipelines combining language and vision

Tomasz Kornuta

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Tags: Computer science, Computer vision, Machine learning, Neural networks, Package, Python

October 27, 2019 by hgpu

Performance Evaluation of Advanced Features in CUDA Unified Memory

Steven W. D. Chien, Ivy B. Peng, Stefano Markidis

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Tags: Benchmarking, Computer science, CUDA, Memory, nVidia, nVidia GeForce GTX 1050, Package, Prefetch, Tesla V100

October 27, 2019 by hgpu

Performance Debugging Frameworks for FPGA High-Level Synthesis

Young-kyu Choi

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Tags: Benchmarking, Computer science, Design space exploration, FPGA, HLS, Performance, Thesis

October 27, 2019 by hgpu

SneakySnake: A Fast and Accurate Universal Genome Pre-Alignment Filter for CPUs, GPUs, and FPGAs

Mohammed Alser, Taha Shahroodi, Juan Gomez-Luna, Can Alkan, Onur Mutlu

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Tags: Algorithms, Computer science, CUDA, FPGA, nVidia, Package, Sequence alignment

October 27, 2019 by hgpu

A Benchmark Set of Highly-efficient CUDA and OpenCL Kernels and its Dynamic Autotuning with Kernel Tuning Toolkit

Filip Petrovič, David Střelák, Jana Hozzová, Jaroslav Oľha, Richard Trembecký, Siegfried Benkner, Jiří Filipovič

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Tags: AMD Radeon RX Vega 56, Auto-Tuning, Benchmarking, Computer science, CUDA, Electron microscopy, Intel Xeon Phi, Microscopy, nVidia, nVidia GeForce GTX 1070, nVidia GeForce GTX 750, nVidia GeForce RTX 2080 Ti, OpenCL, Package, Performance, performance portability, Tesla K20

October 27, 2019 by hgpu

AI Benchmark: All About Deep Learning on Smartphones in 2019

Andrey Ignatov, Radu Timofte, Andrei Kulik, Seungsoo Yang, Ke Wang, Felix Baum, Max Wu, Lirong Xu, Luc Van Gool

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Tags: Android, Benchmarking, Computer science, CUDA, Deep learning, nVidia, OpenCL, Overview

October 20, 2019 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

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high performance computing on graphics processing units: hgpu.org

Applications

Accelerating Stochastic Simulations on GPUs Using OpenCL

Code Optimization on GPUs

JSDoop and TensorFlow.js: Volunteer Distributed Web Browser-Based Neural Network Training

In-memory database acceleration on FPGAs: a survey

Implementing and evaluating an heterogeneous, scalable, tridiagonal linear system solver with OpenCL to target FPGAs, GPUs, and CPUs

Research on OpenCL optimization for FPGA deep learning application

PyTorchPipe: a framework for rapid prototyping of pipelines combining language and vision

Performance Evaluation of Advanced Features in CUDA Unified Memory

Performance Debugging Frameworks for FPGA High-Level Synthesis

SneakySnake: A Fast and Accurate Universal Genome Pre-Alignment Filter for CPUs, GPUs, and FPGAs

A Benchmark Set of Highly-efficient CUDA and OpenCL Kernels and its Dynamic Autotuning with Kernel Tuning Toolkit

AI Benchmark: All About Deep Learning on Smartphones in 2019

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

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