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An execution model for adaptive load-balancing on multicore and multi-GPU systems

Christopher Lauderdale, Robert Springer, Rishi Khan

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Tesla C2070

July 20, 2014 by hgpu

On Development, Feasibility, and Limits of Highly Efficient CPU and GPU Programs in Several Fields

David Rohr

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Performance, Review, Thesis

July 14, 2014 by hgpu

Parallel Implementations for Solving Shortest Path Problem using Bellman-Ford

Gaurav Hajela, Manish Pandey

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Tags: Algorithms, ATI, ATI Radeon HD 6450, Computer science, OpenCL, Path problems

July 12, 2014 by hgpu

Programming-Model Centric Debugging for Multicore Embedded Systems

Kevin Pouget

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Tags: Computer science, CUDA, H.264/AVC, nVidia, OpenCL, Programming techniques, Tesla K20, Thesis, Video decoding

July 11, 2014 by hgpu

Visualization of Large Volumetric Multi-Channel Microscopy Data Streams on Standard PCs

Tobias Brix, Jorg-Stefan Prassni, Klaus Hinrichs

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Tags: ATI, Computer science, Graphics, Microscopy, OpenCL, Rendering, Visualization

July 11, 2014 by hgpu

Random Fields Generation on the GPU with the Spectral Turning Bands Method

Lars Hunger, Biagio Cosenza, Stefan Kimeswenger, Thomas Fahringer

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Tags: Algorithms, Astrophysics, CUDA, FFT, nVidia, nVidia GeForce GTX 550, OpenCL, Spectral methods

July 10, 2014 by hgpu

Utilizing state-of-art NeuroES and GPGPU to optimize Mario AI

Hasse Lovgren

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Tags: ATI, ATI Radeon HD 5870, Computer science, Mathematics, Neural networks, nVidia, nVidia GeForce GTX 580, OpenCL, Package, Performance, Tesla C1060, Thesis

July 9, 2014 by hgpu

GiMMiK – Generating Bespoke Matrix Multiplication Kernels for Various Hardware Accelerators; Applications in High-Order Computational Fluid Dynamics

Bartosz D. Wozniak

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Tags: CUBLAS, CUDA, Fluid dynamics, Linear Algebra, Matrix multiplication, nVidia, OpenCL, Package, Python, Tesla K40, Thesis

July 7, 2014 by hgpu

A Parallelized Implementation for H. 264 Real-time Encoding Scheme

Young Chun Kwon, Chanho Park, Daeseok Oh, WooSuk Jang, Nakhoon Baek

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Tags: H.264/AVC, Image processing, nVidia, nVidia GeForce GTX 560 Ti, OpenCL, Video encoding

July 6, 2014 by hgpu

High-level Parallel Programming Support for Heterogeneous Systems

Bo Wu

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Tags: Computer science, CUDA, Heterogeneous systems, Memory model, nVidia, OpenCL, Tesla C2050, Thesis

July 6, 2014 by hgpu

Writing self-adaptive codes for heterogeneous systems

Jorge F. Fabeiro, Diego Andrade, Basilio B. Fraguela, Ramon Doallo

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Tags: Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, Package, Tesla K20

July 4, 2014 by hgpu

Molecular dynamics simulations through GPU video games technologies

Styliani Loukatou, Louis Papageorgiou, Paraskevas Fakourelis, Arianna Filntisi, Eleftheria Polychronidou, Ioannis Bassis, Vasileios Megalooikonomou, Wojciech Makalowski, Dimitrios Vlachakis, Sophia Kossida

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Tags: Bioinformatics, Biology, Chemistry, CUDA, Medicine, Molecular dynamics, nVidia, OpenCL, Physics

July 4, 2014 by hgpu

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

SYCL Container

Exploring SYCL for batched kernels with memory allocations

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

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

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?

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