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Solving prime-field ECDLPs on GPUs with OpenCL

Erik Boss

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Tags: ATI, ATI Radeon HD 7850, Computer science, nVidia, nVidia GeForce GTX 780, OpenCL, Security, Thesis

September 29, 2015 by hgpu

TABLA: A Unified Template-based Framework for Accelerating Statistical Machine Learning

Divya Mahajan, Jongse Park, Emmanuel Amaro, Hardik Sharma, Amir Yazdanbakhsh, Joon Kim, Hadi Esmaeilzadeh

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Tags: ARM, ASIC, Computer science, FPGA, Machine learning, nVidia, nVidia GeForce GTX 650 Ti, nVidia Jetson TK1, Performance, Tesla K40

September 29, 2015 by hgpu

From Pixels to Torques: Policy Learning using Deep Dynamical Convolutional Networks

John-Alexander M. Assael

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Tags: CNN, Computer science, CUDA, Deep learning, Lua, Machine learning, nVidia, Package, Tesla K40, Thesis

September 26, 2015 by hgpu

A GPU accelerated Barnes-Hut Tree Code for FLASH4

Gunther Lukat, Robi Banerjee

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Tags: Astrophysics, CUDA, Fortran, Gravitation, Instrumentation and Methods for Astrophysics, nVidia, Package, Python, Tesla C2075

September 26, 2015 by hgpu

Efficient Simulation Techniques for Large-Scale Applications

Jen-Cheng Huang

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Tags: Analytical model, Computer science, CUDA, nVidia, nVidia Quadro 6000, Performance, Thesis

September 26, 2015 by hgpu

Fast Exact Bayesian Inference for High-Dimensional Models

Joao Filipe Ferreira, Pablo Lanillos, Jorge Dias

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Tags: Bayesian, Computer science, Computer vision, nVidia, nVidia GeForce GTX 780, OpenCL, Robotics

September 26, 2015 by hgpu

A Survey of CUDA-based Multidimensional Scaling on GPU Architecture

Hasmik Osipyan, Martin Krulis, Stephane Marchand-Maillet

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Tags: Algorithms, Computer science, CUDA, Machine learning, Multidimensional scaling, nVidia, nVidia GeForce GT 740 M, Pattern recognition

September 26, 2015 by hgpu

OpenCL Based Digital Image Projection Acceleration

Bryan Michael Badalamenti

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Tags: Algorithms, Computer science, FPGA, Intel Xeon Phi, OpenCL, Tesla K10, Tesla K40, Thesis

September 24, 2015 by hgpu

A Parallel Framework for Parametric Maximum Flow Problems in Image Segmentation

Vlad Olaru, Mihai Florea, Cristian Sminchisescu

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Tags: Algorithms, Computer science, Computer vision, CUDA, Matlab, nVidia, nVidia GeForce GTX Titan Black, Package, Tesla K40

September 24, 2015 by hgpu

Adaptive and Transparent Cache Bypassing for GPUs

Ang Li, Gert-Jan van den Braak, Akash Kumar, Henk Corporaal

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Tags: Benchmarking, cache, Computer science, Memory, nVidia, nVidia GeForce GTX 460, nVidia GeForce GTX 570, nVidia GeForce GTX 690, nVidia GeForce GTX 750 Ti, nVidia GeForce GTX 980, Performance, PTX, Tesla K40, Tesla K80

September 24, 2015 by hgpu

Overcomplete Dictionary Learning with Jacobi Atom Updates

Paul Irofti, Bogdan Dumitrescu

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Tags: ATI, ATI FirePro V8800, Computer science, Computer vision, OpenCL

September 24, 2015 by hgpu

A Co-Design Framework with OpenCL Support for Low-Energy Wide SIMD Processor

Dongrui She, Yifan He, Luc Waeijen, Henk Corporaal

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Tags: Code generation, Compilers, Computer science, Embedded high-performance computing, OpenCL

September 24, 2015 by hgpu

PROM: an open-source software framework to enhance the robustness and performance of predictive models against such changes during deployment

Enhancing Deployment-Time Predictive Model Robustness for Code Analysis and Optimization

DeepSpeed: a deep learning optimization library that makes distributed training and inference easy, efficient, and effective

Scalable Access-Pattern Aware I/O Acceleration and Multi-Tiered Data Management for HPC and AI Workloads

Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations

SW#SYCL

Analyzing the Performance Portability of SYCL across CPUs, GPUs, and Hybrid Systems with Protein Database Search

tdg-benchs: benchmarks used to test the performance of taskgraph

Leveraging the potential of task-based programming with OpenMP task graphs

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high performance computing on graphics processing units: hgpu.org

Applications

Solving prime-field ECDLPs on GPUs with OpenCL

TABLA: A Unified Template-based Framework for Accelerating Statistical Machine Learning

From Pixels to Torques: Policy Learning using Deep Dynamical Convolutional Networks

A GPU accelerated Barnes-Hut Tree Code for FLASH4

Efficient Simulation Techniques for Large-Scale Applications

Fast Exact Bayesian Inference for High-Dimensional Models

A Survey of CUDA-based Multidimensional Scaling on GPU Architecture

OpenCL Based Digital Image Projection Acceleration

A Parallel Framework for Parametric Maximum Flow Problems in Image Segmentation

Adaptive and Transparent Cache Bypassing for GPUs

Overcomplete Dictionary Learning with Jacobi Atom Updates

A Co-Design Framework with OpenCL Support for Low-Energy Wide SIMD Processor

Recent source codes

PROM: an open-source software framework to enhance the robustness and performance of predictive models against such changes during deployment

DeepSpeed: a deep learning optimization library that makes distributed training and inference easy, efficient, and effective

HPX: a C++ Standard Library for Concurrency and Parallelism

TorchQC: Quantum Dynamics and Machine Learning

Matrix multiplication using Tensor Cores in CUDA

CPP Joules: Energy Measurement tool for CPP/CUDA programs

HPC-Coder-v2

Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations

SW#SYCL

tdg-benchs: benchmarks used to test the performance of taskgraph

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