high performance computing on graphics processing units: hgpu.org

hgpu.org » Applications » Computer science

Improving Locality of Unstructured Mesh Algorithms on GPUs

Andras Attila Sulyok, Gabor Daniel Balogh, Istvan Zoltan Reguly, Gihan R. Mudalige

View

Tags: Algorithms, Computer science, CUDA, Finite element method, Mathematical Software, nVidia, Performance, Tesla P100

February 15, 2018 by hgpu

GPU Accelerated Finite Element Assembly with Runtime Compilation

Tao Cui, Xiaohu Guo, Hui Liu

View

Tags: Computer science, CUDA, Differential equations, FEM, Finite element method, Mathematical Software, Numerical Analysis, nVidia, Partial differential equations, PDEs, Symbolic Computation, Tesla K20, Tesla M2090, Tesla V100

February 15, 2018 by hgpu

Using Meta-heuristics and Machine Learning for Software Optimization of Parallel Computing Systems: A Systematic Literature Review

Suejb Memeti, Sabri Pllana, Alecio Binotto, Joanna Kolodziej, and Ivona Brandic

View

Tags: Computer science, Machine learning, Metaheuristics, parallel computing, software optimization

February 10, 2018 by suejb.memeti

Zorua: Enhancing Programming Ease, Portability, and Performance in GPUs by Decoupling Programming Models from Resource Management

Nandita Vijaykumar, Kevin Hsieh, Gennady Pekhimenko, Samira Khan, Ashish Shrestha, Saugata Ghose, Phillip B. Gibbons, Onur Mutlu

View

Tags: Computer science, CUDA, Hardware Architecture, nVidia, Virtualization

February 10, 2018 by hgpu

Lost in Abstraction: Pitfalls of Analyzing GPUs at the Intermediate Language Level

Anthony Gutierrez, Bradford M. Beckmann, Alexandru Dutu, Joseph Gross, John Kalamatianos, Onur Kayiran, Michael LeBeane, Matthew Poremba, Brandon Potter, Sooraj Puthoor, Matthew D. Sinclair, Mark Wyse, Jieming Yin, Xianwei Zhang, Akshay Jain, Timothy G. Rogers

View

Tags: ATI IL, Computer science, HSA

February 10, 2018 by hgpu

Accelerating Deep Neural Networks on Low Power Heterogeneous Architectures

Manolis Loukadakis, Jose Cano, Michael O'Boyle

View

Tags: ARM, Computer science, Deep learning, Heterogeneous systems, Neural networks, OpenCL

February 10, 2018 by hgpu

Ikra-Cpp: A C++/CUDA DSL for Object-Oriented Programming with Structure-of-Arrays Layout

Matthias Springer, Hidehiko Masuhara

View

Tags: Computer science, CUDA, Data Structures and Algorithms, DSL, nVidia, Package

February 9, 2018 by hgpu

Combined Spatial and Temporal Blocking for High-Performance Stencil Computation on FPGAs Using OpenCL

Hamid Reza Zohouri, Artur Podobas, Satoshi Matsuoka

View

Tags: Computer science, FPGA, OpenCL, Stencil computation

February 9, 2018 by hgpu

MiMatrix: A Massively Distributed Deep Learning Framework on a Petascale High-density Heterogeneous Cluster

Xin Chen, Hua Zhou, Yuxiang Gao, Yu Zhu, Dongyan Wang

View

Tags: Algorithms, Computer science, CUDA, Deep learning, GPU cluster, Heterogeneous systems, nVidia, RDMA, Tesla P100

February 9, 2018 by hgpu

Tuning Streamed Applications on Intel Xeon Phi: A Machine Learning Based Approach

Peng Zhang, Jianbin Fang, Tao Tang, Canqun Yang, Zheng Wang

View

Tags: Benchmarking, Computer science, Intel Xeon Phi, Machine learning, Package, Performance

February 9, 2018 by hgpu

REOH: Runtime Energy Optimization for Heterogeneous Systems

Vi Ngoc-Nha Tran, Tommy Oines, Alexander Horsch, Phuong Hoai Ha

View

Tags: Computer science, Heterogeneous systems, nVidia, nVidia Quadro K620, OpenCL, Package

February 3, 2018 by hgpu

Accelerating recurrent neural network language model based online speech recognition system

Kyungmin Lee, Chiyoun Park, Namhoon Kim, Jaewon Lee

View

Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, RNN, Speech recognition, Tesla K80

February 3, 2018 by hgpu

hpcbench: A set of benchmarking utilities for biomolecular simulation tools

Engineering Supercomputing Platforms for Biomolecular Applications

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

chemtrain: Training Molecular Dynamics Potentials in JAX

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations

microSYCL: SYCL micro-benchmarks repository

Exploring SYCL as a Portability Layer for High-Performance Computing on CPUs

XaaS containers

Acceleration as a Service (XaaS) Source Containers

CASS: Cuda-Amd aSSembly

CASS: Nvidia to AMD Transpilation with Data, Models, and Benchmark

Cluser of smartphones for edge computing application using TensorFlow

Low-cost edge computing using upcycled smartphones

SYCL Container

Exploring SYCL for batched kernels with memory allocations

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

Can Large Language Models Predict Parallel Code Performance?

See all packages

* * *

* * *

HGPU group © 2010-2025 hgpu.org

All rights belong to the respective authors

Login | Sitemap | Feedback | Policy

Contact us:

contact@hpgu.org