high performance computing on graphics processing units: hgpu.org

hgpu.org » Applications

Android Malware Classification Using Parallelized Machine Learning Methods

Lifan Xu

View

Tags: ATI, ATI Radeon HD 7970, Computer science, Deep learning, Machine learning, Neural networks, nVidia, OpenCL, OpenMP, Security, Tesla C2050, Thesis

December 31, 2016 by hgpu

Log File Regular Expression Pattern Matching And Capture With GPUs

António Luís Pinto Silva

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 850 M, Package, pattern matching, Thesis

December 22, 2016 by xhgpu

Toward Automatic Translation: From OpenACC to OpenMP 4

Nawrin Sultana

View

Download (PDF)

Tags: Algorithms, Code generation, Compilers, Computer science, nVidia, OpenACC, OpenMP, Tesla K40, Thesis

December 17, 2016 by hgpu

Towards Comprehensive Parametric Code Generation Targeting Graphics Processing Units in Support of Scientific Computation

Ning Xie

View

Download (PDF)

Tags: Code generation, Computer science, CUDA, nVidia, OpenMP, Tesla M2050, Thesis

December 14, 2016 by hgpu

Optimization of Pattern Matching Algorithms for Multi- and Many-Core Platforms

Pedro Miguel Marques Pereira

View

Download (PDF)

Tags: Algorithms, AMD Radeon R9 290X, ARM, ATI, ATI Radeon HD 7970, Compression, Computer science, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 570 M, nVidia GeForce GTX 680, nVidia GeForce GTX 750 Ti, nVidia GeForce GTX Titan Black, nVidia Jetson TK1, OpenCL, OpenMP, Thesis

November 30, 2016 by hgpu

Performance Analysis of CUDA and OpenCL By Implementation of Cryptographic Algorithms

Manas Mahapatra

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia GeForce GT 525 M, OpenCL, Performance, Security, Thesis

November 23, 2016 by hgpu

Processing OLTP Workloads on Hybrid CPU/GPU Systems

Mudit Bhatnagar

View

Download (PDF)

Tags: Computer science, Databases, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GT 640, nVidia GeForce GTX Titan, OpenCL, Thesis

November 22, 2016 by hgpu

Automatic code generation methods applied to numerical linear algebra in high performance computing

M. Ian Masliah

View

Download (PDF)

Tags: Algorithms, Code generation, Computer science, CUDA, Heterogeneous systems, Linear Algebra, Mixed precision, nVidia, nVidia Tegra TX1, Programming techniques, Tesla C2075, Tesla K40, Thesis

November 16, 2016 by hgpu

UNICORN: A Bulk Synchronous Programming Model, Framework and Runtime for Hybrid CPU-GPU Clusters

Tarun Beri

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, FFT, GPU cluster, Linear Algebra, Matrix multiplication, nVidia, Package, Prefetch, Tesla M2070, Thesis

November 5, 2016 by hgpu

Classification Performance of Convolutional Neural Networks

Niklas Mattsson

View

Download (PDF)

Tags: Computer science, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 960, nVidia Tegra TX1, Performance, Python, Theano, Thesis

November 1, 2016 by hgpu

GPU Performance Modeling and Optimization

Ang Li

View

Download (PDF)

Tags: Computer science, CUDA, Hardware Architecture, nVidia, Performance, PTX, Thesis

October 29, 2016 by hgpu

Sparse-Matrix support for the SkePU library for portable CPU/GPU programming

Vishist Sharma

View

Download (PDF)

Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenMP, Sparse matrix, Tesla M2050, Thesis

October 22, 2016 by hgpu

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

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

GigaAPI for GPU Parallelization

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

Shamrock: Multi-GPU hydrodynamics for astrophysics

The Shamrock code: I- Smoothed Particle Hydrodynamics on GPUs

LLMPerf: GPU Performance Modeling meets Large Language Models

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

* * *

high performance computing on graphics processing units: hgpu.org

Theses

Android Malware Classification Using Parallelized Machine Learning Methods

Log File Regular Expression Pattern Matching And Capture With GPUs

Toward Automatic Translation: From OpenACC to OpenMP 4

Towards Comprehensive Parametric Code Generation Targeting Graphics Processing Units in Support of Scientific Computation

Optimization of Pattern Matching Algorithms for Multi- and Many-Core Platforms

Performance Analysis of CUDA and OpenCL By Implementation of Cryptographic Algorithms

Processing OLTP Workloads on Hybrid CPU/GPU Systems

Automatic code generation methods applied to numerical linear algebra in high performance computing

UNICORN: A Bulk Synchronous Programming Model, Framework and Runtime for Hybrid CPU-GPU Clusters

Classification Performance of Convolutional Neural Networks

GPU Performance Modeling and Optimization

Sparse-Matrix support for the SkePU library for portable CPU/GPU programming

Recent source codes

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

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

DuoReduce: MLIR's benchmark

Shamrock: Multi-GPU hydrodynamics for astrophysics

LLMPerf: GPU Performance Modeling meets Large Language Models

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

Celerity Runtime: High-level C++ for Accelerator Clusters

wgpy: WebGL accelerated numpy-compatible array library for web browser

Microbenchmarking OpenMP target offload with Catch2

Most viewed papers (last 30 days)