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Soft GPGPUs for Embedded FPGAs: An Architectural Evaluation

Kevin Andryc, Tedy Thomas, Russell Tessier

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Tags: Benchmarking, Computer science, CUDA, DSP, FPGA

June 22, 2016 by hgpu

YodaNN: An Ultra-Low Power Convolutional Neural Network Accelerator Based on Binary Weights

Renzo Andri, Lukas Cavigelli, Davide Rossi, Luca Benini

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Tags: Computer science, Computer vision, Deep learning, Hardware Architecture, Neural networks

June 21, 2016 by hgpu

Combinatorial Optimization of Work Distribution on Heterogeneous Systems

Suejb Memeti, Sabri Pllana

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Tags: Computer science, Heterogeneous systems, Intel Xeon Phi, Machine learning, Optimization

June 21, 2016 by hgpu

cltorch: a Hardware-Agnostic Backend for the Torch Deep Neural Network Library, Based on OpenCL

Hugh Perkins

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Tags: Algorithms, CNN, Computer science, Deep learning, Machine learning, Neural networks, nVidia, nVidia GRID K520, OpenCL, Package

June 21, 2016 by hgpu

Acceleration of Statistical Detection of Zero-day Malware in the Memory Dump Using CUDA-enabled GPU Hardware

Igor Korkin, Iwan Nesterow

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Tags: Computer science, CUDA, nVidia, Security

June 21, 2016 by hgpu

NCAM: Near-Data Processing for Nearest Neighbor Search

Vincent T. Lee, Carlo C. del Mundo, Armin Alaghi, Luis Ceze, Mark Oskin, Ali Farhadi

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Tags: Computer science, CUDA, Machine learning, Nearest neighbour, nVidia, nVidia GeForce GTX Titan X, nVidia Jetson TK1

June 16, 2016 by hgpu

Splotch: porting and optimizing for the Xeon Phi

Timothy Dykes, Claudio Gheller, Marzia Rivi, Mel Krokos

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Tags: Computer science, CUDA, Intel Xeon Phi, Numerical simulation, nVidia, OpenCL, Package, Tesla K20, Visualization

June 16, 2016 by hgpu

Multi-Tenant Virtual GPUs for Optimising Performance of a Financial Risk Application

Javier Prades, Blesson Varghese, Carlos Reano, Federico Silla

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Tags: Computer science, CUDA, Finance, nVidia, Tesla K20

June 16, 2016 by hgpu

Omnivore: An Optimizer for Multi-device Deep Learning on CPUs and GPUs

Stefan Hadjis, Ce Zhang, Ioannis Mitliagkas, Christopher Re

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan X

June 16, 2016 by hgpu

Performance-Portable Many-Core Plasma Simulations: Porting PIConGPU to OpenPower and Beyond

Erik Zenker, Rene Widera, Axel Hubl, Guido Juckeland, Andreas Knupfer, Wolfgang E. Nagel, Michael Bussmann

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenMP, Package, Particle-in-cell methods, Physics, Plasma physics, Programming techniques, Tesla K80

June 14, 2016 by hgpu

OpenCL-Based Erasure Coding on Heterogeneous Architectures

Guoyang Chen, Huiyang Zhou, Xipeng Shen, Josh Gahm, Narayan Venkat, Skip Booth, John Marshall

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Tags: Algorithms, APU, ATI, Computer science, Error recovery, FPGA, Heterogeneous systems, nVidia, OpenCL, Tesla K40

June 14, 2016 by hgpu

Processing Big Data in Main Memory and on GPU

Meisam Fathi Salmi

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Tags: big data, Computer science, CUDA, Hadoop, Java, OpenCL, Scala, Spark, Thesis

June 14, 2016 by hgpu

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

EnergyUCB-Bandit

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

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high performance computing on graphics processing units: hgpu.org

Applications

Soft GPGPUs for Embedded FPGAs: An Architectural Evaluation

YodaNN: An Ultra-Low Power Convolutional Neural Network Accelerator Based on Binary Weights

Combinatorial Optimization of Work Distribution on Heterogeneous Systems

cltorch: a Hardware-Agnostic Backend for the Torch Deep Neural Network Library, Based on OpenCL

Acceleration of Statistical Detection of Zero-day Malware in the Memory Dump Using CUDA-enabled GPU Hardware

NCAM: Near-Data Processing for Nearest Neighbor Search

Splotch: porting and optimizing for the Xeon Phi

Multi-Tenant Virtual GPUs for Optimising Performance of a Financial Risk Application

Omnivore: An Optimizer for Multi-device Deep Learning on CPUs and GPUs

Performance-Portable Many-Core Plasma Simulations: Porting PIConGPU to OpenPower and Beyond

OpenCL-Based Erasure Coding on Heterogeneous Architectures

Processing Big Data in Main Memory and on GPU

Recent source codes

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

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

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

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

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

EnergyUCB-Bandit

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