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liquidSVM: A Fast and Versatile SVM package

Ingo Steinwart, Philipp Thomann

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Tags: Computer science, CUDA, Java, nVidia, Package, Python, Spark

February 26, 2017 by hgpu

Development of JavaScript-based deep learning platform and application to distributed training

Masatoshi Hidaka, Ken Miura, Tatsuya Harada

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Tags: Computer science, Deep learning, Java, Neural networks, nVidia, OpenCL, Package, Tesla K80, WebCL

February 10, 2017 by hgpu

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

Tian Zhao, Xiaobing Huang, Yu Cao

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

January 10, 2017 by hgpu

Fast and Energy-Efficient CNN Inference on IoT Devices

Mohammad Motamedi, Daniel Fong, Soheil Ghiasi

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Tags: CNN, Computer science, Deep learning, Java, Machine learning, Neural networks, Package, RenderScript, SoC

November 25, 2016 by hgpu

FastCollect: Offloading Generational Garbage Collection to Integrated GPUs

Abhinav, Rupesh Nasre

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Tags: Algorithms, Benchmarking, Computer science, Intel HD Graphics 4000, Java, OpenCL, Performance

September 27, 2016 by hgpu

Agent-Based Modeling on High Performance Computing Architectures

Nuno Fachada

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Tags: cpu, GPGPU, HPC, Java, OpenCL, Thesis

September 16, 2016 by fakenmc

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

Microbenchmarks for GPU characteristics: the occupancy roofline and the pipeline model

Jan Lemeire, Jan G. Cornelis, Laurent Segers

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Tags: Benchmarking, Computer science, Java, nVidia, nVidia GeForce GT 640, nVidia Quadro K620, OpenCL, Package

May 9, 2016 by hgpu

Automatic Parallelization: Executing Sequential Programs on a Task-Based Parallel Runtime

Alcides Fonseca, Bruno Cabral, Joao Rafael, Ivo Correia

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Tags: Computer science, Java, OpenCL, Package, Programming Languages

April 29, 2016 by hgpu

Hadoop Mapreduce OpenCL Plugin

Vivek Viswanathan

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Tags: AMD Radeon R9 290X, ATI, Computer science, Heterogeneous systems, Java, MapReduce, OpenCL

March 3, 2016 by hgpu

DeepSpark: Spark-Based Deep Learning Supporting Asynchronous Updates and Caffe Compatibility

Hanjoo Kim, Jaehong Park, Jaehee Jang, Sungroh Yoon

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Tags: Caffe, Computer science, CUDA, Deep learning, Distributed computing, Java, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 970, Package

March 1, 2016 by hgpu

DIANNE: Distributed Artificial Neural Networks for the Internet of Things

Elias De Coninck, Tim Verbelen, Bert Vankeirsbilck, Steven Bohez, Sam Leroux, Pieter Simoens

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Java, Neural networks, nVidia, nVidia GeForce GTX 980, nVidia Jetson TK1, Package

February 25, 2016 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

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high performance computing on graphics processing units: hgpu.org

liquidSVM: A Fast and Versatile SVM package

Development of JavaScript-based deep learning platform and application to distributed training

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

Fast and Energy-Efficient CNN Inference on IoT Devices

FastCollect: Offloading Generational Garbage Collection to Integrated GPUs

Agent-Based Modeling on High Performance Computing Architectures

Processing Big Data in Main Memory and on GPU

Microbenchmarks for GPU characteristics: the occupancy roofline and the pipeline model

Automatic Parallelization: Executing Sequential Programs on a Task-Based Parallel Runtime

Hadoop Mapreduce OpenCL Plugin

DeepSpark: Spark-Based Deep Learning Supporting Asynchronous Updates and Caffe Compatibility

DIANNE: Distributed Artificial Neural Networks for the Internet of Things

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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