high performance computing on graphics processing units: hgpu.org

hgpu.org » PTX

KernelGen – the design and implementation of a next generation compiler platform for accelerating numerical models on GPUs

Dmitry Mikushin, Nikolay Likhogrud, Eddy Zheng Zhang, Christopher Bergstrom

View

Download (PDF)

Source codes

Tags: Code generation, Compilers, Computer science, CUDA, Fortran, nVidia, OpenACC, Package, PTX, Tesla K20

July 15, 2013 by hgpu

A model of dynamic compilation for heterogeneous compute platforms

Andrew Kerr

View

Download (PDF)

Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 280, OpenCL, PTX, Thesis

June 29, 2013 by hgpu

GPU Programming in Rust: Implementing High Level Abstractions in a Systems Level Language

Eric Holk, Milinda Pathirage, Arun Chauhan, Andrew Lumsdaine, Nicholas D. Matsakis

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce GTX 460, OpenCL, Package, PTX

June 17, 2013 by hgpu

Towards shared memory consistency models for GPUs

Tyler Sorensen

View

Download (PDF)

Tags: Computer science, CUDA, Memory model, nVidia, Performance, PTX, Thesis

June 4, 2013 by hgpu

Fast Makespan Estimation for GPU Threads on a Single Streaming Multiprocessor

Kostiantyn Berezovskyi, Konstantinos Bletsas, Stefan M. Petters

View

Download (PDF)

Tags: Computer science, CUDA, Metaheuristics, nVidia, nVidia GeForce GTX 680, PTX, Timing analysis

April 22, 2013 by hgpu

CUDASW++ 3.0: accelerating Smith-Waterman protein database search by coupling CPU and GPU SIMD instructions

Yongchao Liu, Adrianto Wirawan, Bertil Schmidt

View

Download (PDF)

Source codes

Tags: Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX 690, Package, PTX, Sequence alignment, Smith-Waterman algorithm

April 10, 2013 by hgpu

Kernelet: High-Throughput GPU Kernel Executions with Dynamic Slicing and Scheduling

Jianlong Zhong, Bingsheng He

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 680, PTX, Task scheduling, Tesla C2050

March 23, 2013 by hgpu

Just-in-time Acceleration of JavaScript

Uday Pitambare, Arun Chauhan, Saurabh Malviya

View

Download (PDF)

Tags: Computer science, Java, nVidia, Performance, PTX, Tesla C1060

March 12, 2013 by hgpu

Software Reliability Enhancements for GPU Applications

Si Li, Naila Farooqui, Sudhakar Yalamanchili

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 660 Ti, PTX

February 2, 2013 by hgpu

Scaling CUDA for Distributed Heterogeneous Processors

Siu Kwan Lam

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Heterogeneous systems, Memory model, MPI, nVidia, Package, PTX, Python, Thesis

July 20, 2012 by hgpu

Analyzing the CUDA Applications with its Latency and Bandwidth Tolerance

V.N. Pistulkar, C.A. Uttarwar

View

Download (PDF)

Tags: Computer science, CUDA, GPU cluster, nVidia, PTX, Tesla C1060

July 5, 2012 by hgpu

Parallel Cryptanalysis

Ruben Falko Niederhagen

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, MPI, nVidia, nVidia GeForce GTX 295, PTX, Security, Thesis

May 12, 2012 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

KernelGen – the design and implementation of a next generation compiler platform for accelerating numerical models on GPUs

A model of dynamic compilation for heterogeneous compute platforms

GPU Programming in Rust: Implementing High Level Abstractions in a Systems Level Language

Towards shared memory consistency models for GPUs

Fast Makespan Estimation for GPU Threads on a Single Streaming Multiprocessor

CUDASW++ 3.0: accelerating Smith-Waterman protein database search by coupling CPU and GPU SIMD instructions

Kernelet: High-Throughput GPU Kernel Executions with Dynamic Slicing and Scheduling

Just-in-time Acceleration of JavaScript

Software Reliability Enhancements for GPU Applications

Scaling CUDA for Distributed Heterogeneous Processors

Analyzing the CUDA Applications with its Latency and Bandwidth Tolerance

Parallel Cryptanalysis

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

Most viewed papers (last 30 days)