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Solving Batched Linear Programs on GPU and Multicore CPU

Amit Gurung, Rajarshi Ray

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

September 27, 2016 by hgpu

Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, Jeffrey Dean

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Tags: Computer science, CUDA, Deep learning, LSTM, Machine learning, NLP, nVidia, Tesla K80

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

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

September 22, 2016 by hgpu

Tuning Stencil Codes in OpenCL for FPGAs

Qi Jia, Huiyang Zhou

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Tags: Computer science, FPGA, Heterogeneous systems, OpenCL, Package, Stencil computation

September 22, 2016 by hgpu

Characterization of Speech Recognition Systems on GPU Architectures

Albert Segura Salvador

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

September 22, 2016 by hgpu

Efficient dictionary learning implementation on the GPU using OpenCL

Paul Irofti

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Tags: Algorithms, Computer science, Machine learning, OpenCL, Signal processing

September 22, 2016 by hgpu

MCS 572: Introduction to Supercomputing

Jan Verschelde

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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

Parallel Computational Fluid Dynamics With the Intel Xeon Phi Coprocessor

Adam O'Brien

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Tags: cfd, Fluid dynamics, Intel Xeon Phi, OpenMP

September 20, 2016 by hgpu

Acceleration of Block-Aware Matrix Factorization on Heterogeneous Platforms

Gregory W. Somers

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Tags: ATI, ATI Radeon HD 7950, Computer science, Factorization, Heterogeneous systems, Linear Algebra, OpenCL, Thesis

September 20, 2016 by hgpu

A Compiler for Throughput Optimization of Graph Algorithms on GPUs

Sreepathi Pai, Keshav Pingali

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

September 20, 2016 by hgpu

Feynman Machine: The Universal Dynamical Systems Computer

Eric Laukien, Richard Crowder, Fergal Byrne

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Tags: AMD Radeon R9 290, Artificial intelligence, ATI, Computer science, Deep learning, Mathematics, Neuroscience, OpenCL

September 20, 2016 by hgpu

Runtime Support for Adaptive Power Capping on Heterogeneous SoCs

Yun Wu, Dimitrios S. Nikolopoulos, Roger Woods

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Tags: ARM, Benchmarking, Computer science, Energy-efficient computing, FPGA, Heterogeneous systems, OpenCL, OpenMP, SoC

September 20, 2016 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

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