high performance computing on graphics processing units: hgpu.org

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hgpu.org » Applications » Computer science

Application of Deep-Learning to Compiler-Based Graphs

Tristan Vanderbruggen

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Tags: Compilers, Computer science, Deep learning, Graph theory, Machine learning, Neural networks, nVidia, nVidia GRID K520, OpenCL, Thesis

July 7, 2018 by hgpu

Calamari – A High-Performance Tensorflow-based Deep Learning Package for Optical Character Recognition

Christoph Wick, Christian Reul, Frank Puppe

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Tags: Computer science, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, Package, Python, TensorFlow

July 7, 2018 by hgpu

An Efficient Dispatcher for Large Scale GraphProcessing on OpenCL-based FPGAs

Chengbo Yang

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Tags: Computer science, FPGA, Graph theory, OpenCL, Task scheduling

July 7, 2018 by hgpu

Beyond Straightforward Vectorization of Lightweight Data Compression Algorithms for Larger Vector Sizes

Johannes Pietrzyk, Annett Ungethum, Dirk Habich, Wolfgang Lehner

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Tags: Algorithms, Compression, Computer science, Intel Xeon Phi

July 5, 2018 by hgpu

Exploration of Low Numeric Precision Deep Learning Inference Using Intel FPGAs

Philip Colangelo, Nasibeh Nasiri, Asit Mishra, Eriko Nurvitadhi, Martin Margala, Kevin Nealis

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

July 5, 2018 by hgpu

Evaluating the Efficiency of CPUs, GPUs and FPGAs on a Near-Duplicate Document Detection Via OpenCL

Ercan Canhasi

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Tags: Computer science, FPGA, Machine learning, NLP, OpenCL

July 5, 2018 by hgpu

A Survey on Agent-based Simulation using Hardware Accelerators

Jiajian Xiao, Philipp Andelfinger, David Eckhoff, Wentong Cai, Alois Knoll

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, OpenCL, Overview

July 5, 2018 by hgpu

XGBoost: Scalable GPU Accelerated Learning

Rory Mitchell, Andrey Adinets, Thejaswi Rao, Eibe Frank

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Tags: Computer science, CUDA, Java, Machine learning, nVidia, Package, Python, R, Scala, Tesla V100

July 5, 2018 by hgpu

Directive-Based, High-Level Programming and Optimizations for High-Performance Computing with FPGAs

Jacob Lambert, Seyong Lee, Jungwon Kim, Jeffrey S. Vetter, Allen D. Malony

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Tags: Computer science, FPGA, nVidia, OpenACC, OpenCL, Tesla K40

July 1, 2018 by hgpu

Analysis-driven Engineering of Comparison-based Sorting Algorithms on GPUs

Ben Karsin, Volker Weichert, Henri Casanova, John Iacono, Nodari Sitchinava

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 770, nVidia Quadro M 4000, Performance, Sorting, Tesla K40

July 1, 2018 by hgpu

Reducing the Cost of Heuristic Generation with Machine Learning

William F. Ogilvie

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Tags: Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX Titan, OpenCL, Thesis

July 1, 2018 by hgpu

Compiler Fuzzing through Deep Learning

Chris Cummins, Pavlos Petoumenos, Hugh Leather, Alastair Murray

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Tags: Compilers, Computer science, Deep learning, Intel Xeon Phi, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 780, OpenCL, Package

July 1, 2018 by hgpu

True 4-Bit Quantized CNN Training on CPU

True 4-Bit Quantized Convolutional Neural Network Training on CPU: Achieving Full-Precision Parity

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

Hunting CUDA Bugs at Scale with cuFuzz

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery

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

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high performance computing on graphics processing units: hgpu.org

Applications

Application of Deep-Learning to Compiler-Based Graphs

Calamari – A High-Performance Tensorflow-based Deep Learning Package for Optical Character Recognition

An Efficient Dispatcher for Large Scale GraphProcessing on OpenCL-based FPGAs

Beyond Straightforward Vectorization of Lightweight Data Compression Algorithms for Larger Vector Sizes

Exploration of Low Numeric Precision Deep Learning Inference Using Intel FPGAs

Evaluating the Efficiency of CPUs, GPUs and FPGAs on a Near-Duplicate Document Detection Via OpenCL

A Survey on Agent-based Simulation using Hardware Accelerators

XGBoost: Scalable GPU Accelerated Learning

Directive-Based, High-Level Programming and Optimizations for High-Performance Computing with FPGAs

Analysis-driven Engineering of Comparison-based Sorting Algorithms on GPUs

Reducing the Cost of Heuristic Generation with Machine Learning

Compiler Fuzzing through Deep Learning

Recent source codes

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

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

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