high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming » CUDA

Solving Batched Linear Programs on GPU and Multicore CPU

Amit Gurung, Rajarshi Ray

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, nVidia, Package, Performance, Tesla K40

September 27, 2016 by hgpu

Bridging the Semantic Gaps of GPU Acceleration for Scaleout CNN-based Big Data Processing: Think Big, See Small

Mingcong Song, Yang Hu, Yunlong Xu, Chao Li, Huixiang Chen, Jingling Yuan, Tao Li

View

Download (PDF)

Tags: Analytical model, big data, Computer science, CUDA, Deep learning, Distributed computing, Neural networks, nVidia, Tesla K20

September 22, 2016 by hgpu

Characterization of Speech Recognition Systems on GPU Architectures

Albert Segura Salvador

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Deep learning, GPGPU-sim, nVidia, nVidia GeForce GTX 980, Speech recognition, Thesis

September 22, 2016 by hgpu

MCS 572: Introduction to Supercomputing

Jan Verschelde

View

Download (PDF)

Source codes

Tags: Algorithms, Computer science, CUDA, MPI, Numerical Analysis, nVidia, nVidia GeForce 9400 M, OpenMP, Package, Tesla C2050, Tesla C2070, Tesla K20, Tutorial

September 22, 2016 by hgpu

A Compiler for Throughput Optimization of Graph Algorithms on GPUs

Sreepathi Pai, Keshav Pingali

View

Download (PDF)

Tags: Compilers, Computer science, CUDA, Graph theory, nVidia, Tesla K40

September 20, 2016 by hgpu

The CUDA LATCH Binary Descriptor: Because Sometimes Faster Means Better

Christopher Parker, Matthew Daiter, Kareem Omar, Gil Levi, Tal Hassner

View

Download (PDF)

Source codes

Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 1080, Package

September 17, 2016 by hgpu

Parallel Dynamics Computation using Prefix Sum Operations

Yajue Yang, Yuanqing Wu, Jia Pan

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, Robotics, Tesla K40

September 17, 2016 by hgpu

Data Analysis of Minimally-Structured Heterogeneous Logs: An experimental study of log template extraction and anomaly detection based on Recurrent Neural Network and Naive Bayes

Chang Liu

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Deep learning, LSTM, NLP, nVidia, Package, RNN, Thesis

September 13, 2016 by hgpu

OpenMP as a High-Level Specification Language for Parallelism And its use in Evaluating Parallel Programming Systems

Max Grossman, Jun Shirako, Vivek Sarkar

View

Download (PDF)

Source codes

Tags: Code generation, Compilers, Computer science, CUDA, LLVM, nVidia, OpenMP, Package

September 13, 2016 by hgpu

An efficient numerical method for solving the Boltzmann equation in multidimensions

Giacomo Dimarco, Raphael Loubere, Jacek Narski, Thomas Rey

View

Download (PDF)

Tags: Algorithms, Boltzmann equation, cfd, CUDA, Fluid dynamics, Mathematics, MPI, Numerical Analysis, nVidia, OpenMP, Physics

September 13, 2016 by hgpu

Scheduling of Linear Algebra Kernels on Multiple Heterogeneous Resources

Olivier Beaumont, Terry Cojean, Lionel Eyraud-Dubois, Abdou Guermouche, Suraj Kumar

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Factorization, Heterogeneous systems, Linear Algebra, nVidia, Tesla K40

September 13, 2016 by hgpu

A New Architecture for Optimization Modeling Frameworks

Matt Wytock, Steven Diamond, Felix Heide, Stephen Boyd

View

Download (PDF)

Source codes

Tags: Algorithms, CUDA, Deep learning, Linear Algebra, Mathematics, nVidia, nVidia GeForce GTX Titan X, Package, Python

September 13, 2016 by hgpu

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Accelerating a Linear Programming Algorithm on AMD GPUs

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Towards Efficient and Practical GPU Multitasking in the Era of LLM

Bandicoot: C++ library for GPU accelerated linear algebra

Bandicoot: A Templated C++ Library for GPU Linear Algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Programming

Solving Batched Linear Programs on GPU and Multicore CPU

Bridging the Semantic Gaps of GPU Acceleration for Scaleout CNN-based Big Data Processing: Think Big, See Small

Characterization of Speech Recognition Systems on GPU Architectures

MCS 572: Introduction to Supercomputing

A Compiler for Throughput Optimization of Graph Algorithms on GPUs

The CUDA LATCH Binary Descriptor: Because Sometimes Faster Means Better

Parallel Dynamics Computation using Prefix Sum Operations

Data Analysis of Minimally-Structured Heterogeneous Logs: An experimental study of log template extraction and anomaly detection based on Recurrent Neural Network and Naive Bayes

OpenMP as a High-Level Specification Language for Parallelism And its use in Evaluating Parallel Programming Systems

An efficient numerical method for solving the Boltzmann equation in multidimensions

Scheduling of Linear Algebra Kernels on Multiple Heterogeneous Resources

A New Architecture for Optimization Modeling Frameworks

Recent source codes

CrossTL: Universal Programming Language & Translator

TBD-GPU

DG-SWEM - The Discontinuous Galerkin Shallow Water Equation Model

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Bandicoot: C++ library for GPU accelerated linear algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Most viewed papers (last 30 days)