Tesla T20 | hgpu.org

hgpu.org » Tesla T20

GPU Declarative Framework

Robert W. Senser, Jr.

View

Tags: AMD Radeon R9 270, ATI, Code generation, Computer science, Filtering, Image processing, Matrix inversion, nVidia, OpenCL, Sorting, Tesla S2050, Tesla T20, Thesis

December 1, 2014 by hgpu

A second generation of DEFG: Declarative Framework for GPUs

Robert Senser, Tom Altman

View

Download (PDF)

Tags: Computer science, High-level Languages, nVidia, OpenCL, Tesla T20

July 4, 2014 by hgpu

DEF-G: Declarative Framework for GPU Environment

Robert Senser, Tom Altman

View

Download (PDF)

Tags: Code generation, Computer science, Filtering, nVidia, nVidia GeForce GT 430, OpenCL, Tesla T20

January 5, 2014 by hgpu

Median Based Parallel Steering Kernel Regression for Image Reconstruction

G Dada Khalandhar, V Sai Ram, M Srinivasa Rao, Lalith Srikanth C, Pallav Kumar Baruah, Balasubramanian S, R Raghunatha Sarma

View

Download (PDF)

Tags: Algorithms, CUDA, Image processing, Image reconstruction, nVidia, Tesla T20

August 23, 2013 by hgpu

A Computationally Efficient Parallel Kernel Regression for Image Reconstruction

V.Sairam, M.Srinivasa Rao, G.Dada Khalandhar, L.Srikanth, P.K.Baruah, R.R.Sarma

View

Download (PDF)

Tags: Algorithms, CUDA, Image processing, Image reconstruction, Medicine, nVidia, Tesla T20, Tomography

December 8, 2012 by hgpu

Sparse direct solvers with accelerators over DAG runtimes

Xavier Lacoste, Pierre Ramet, Mathieu Faverge, Yamazaki Ichitaro, Jack Dongarra

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Factorization, Linear Algebra, nVidia, Sparse direct solvers, Tesla T20

May 24, 2012 by hgpu

Cost Efficient PageRank Computation using GPU

Praveen K., Vamshi Krishna K., Anil Sri Harsha B., S. Balasubramanian, P.K. Baruah

View

Download (PDF)

Tags: Algorithms, Computer science, CUBLAS, CUDA, List ranking, nVidia, Sparse matrix, Tesla T20

January 24, 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

GPU Declarative Framework

A second generation of DEFG: Declarative Framework for GPUs

DEF-G: Declarative Framework for GPU Environment

Median Based Parallel Steering Kernel Regression for Image Reconstruction

A Computationally Efficient Parallel Kernel Regression for Image Reconstruction

Sparse direct solvers with accelerators over DAG runtimes

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)