high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications

Decoding with Finite-State Transducers on GPUs

Arturo Argueta, David Chiang

View

Tags: Algorithms, Computer science, CUDA, Machine learning, NLP, nVidia, nVidia GeForce GTX Titan X, Package, Speech recognition, Tesla K40, Tesla P100

January 16, 2017 by hgpu

Parallelization of BVH and BSP on the GPU

Martin Imre

View

Tags: 3D Graphics and Realism, Algorithms, Computer science, nVidia, nVidia GeForce GTX 680, OpenCL, Raytracing, Rendering, Sorting, Thesis, Visualization

January 10, 2017 by hgpu

Software Prefetching for Indirect Memory Accesses

Sam Ainsworth, Timothy M. Jones

View

Tags: ARM, Benchmarking, Compilers, Computer science, Intel Xeon Phi, LLVM, MPI, Package, Prefetch

January 10, 2017 by hgpu

DeepDSL: A Compilation-based Domain-Specific Language for Deep Learning

Tian Zhao, Xiaobing Huang, Yu Cao

View

Tags: Computer science, CUDA, Deep learning, DSL, Java, Machine learning, nVidia, Package, Programming Languages, Scala, Tesla K40

January 10, 2017 by hgpu

An FPGA Accelerator for Molecular Dynamics Simulation Using OpenCL

Hasitha Muthumala Waidyasooriya, Masanori Hariyama, Kota Kasahara

View

Tags: Chemistry, FPGA, Heterogeneous systems, Molecular dynamics, OpenCL, Performance, Physics

January 10, 2017 by hgpu

GPU SQL Query Accelerator

Keh Kok Yong, Hong Hoe Ong, Vooi Voon Yap

View

Tags: big data, Cloud, Computer science, CUDA, Databases, nVidia, Tesla K20

January 10, 2017 by hgpu

Synchronization and Coordination in Heterogeneous Processors

Joel Hestness

View

Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 860 M, OpenCL, Thesis

January 8, 2017 by hgpu

A Framework for Dense Triangular Matrix Kernels on Various Manycore Architectures

Ali Charara, David Keyes, Hatem Ltaief

View

Tags: BLAS, Computer science, CUDA, Intel Xeon Phi, Linear Algebra, Matrix multiplication, nVidia, Package, Tesla K40

January 8, 2017 by hgpu

Akid: A Library for Neural Network Research and Production from a Dataism Approach

Shuai Li

View

Tags: Computer science, CUDA, Deep learning, Distributed computing, Machine learning, Neural networks, nVidia, Package, Python, TensorFlow

January 8, 2017 by hgpu

Communication and Coordination Paradigms for Highly-Parallel Accelerators

Marc S. Orr

View

Tags: Computer science, CUDA, OpenCL, Thesis

January 8, 2017 by hgpu

Gunrock: GPU Graph Analytics

Yangzihao Wang, Yuechao Pan, Andrew Davidson, Yuduo Wu, Carl Yang, Leyuan Wang, Muhammad Osama, Chenshan Yuan, Weitang Liu, Andy T. Riffel, John D. Owens

View

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

January 8, 2017 by hgpu

Deep Neural Networks to Enable Real-time Multimessenger Astrophysics

Daniel George, E. A. Huerta

View

Tags: Algorithms, Artificial intelligence, Astrophysics, CUDA, Deep learning, Filtering, Gravitation, Gravitational waves, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080, Physics, Signal processing, Tesla K40, Tesla P100

January 4, 2017 by hgpu

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

Kokkidio: Fast, expressive, portable code, based on Kokkos and Eigen

LLaMA-Inference-Bench

LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators

MATAR

On a Simplified Approach to Achieve Parallel Performance and Portability Across CPU and GPU Architectures

Over-synchronization in GPU Programs

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

See all packages

* * *

* * *

HGPU group © 2010-2024 hgpu.org

All rights belong to the respective authors

Login | Sitemap | Feedback | Policy

Contact us: