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Implementation and Performance Analysis of Many-body Quantum Chemical Methods on the Intel Xeon Phi Coprocessor and NVIDIA GPU Accelerator

Bobo Shi

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Tags: Algorithms, Chemistry, Computational chemistry, CUDA, Intel Xeon Phi, nVidia, OpenMP, Tesla K40, Thesis

September 5, 2016 by hgpu

Improving the Performance of the Contextual Spaces Re-Ranking Algorithm on Heterogeneous Systems

Flavia Pisani, Daniel C. G. Pedronette, Ricardo da S. Torres, Edson Borin

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Tags: Algorithms, ATI, ATI Radeon HD 6550, Computer science, Heterogeneous systems, List ranking, OpenCL

September 5, 2016 by hgpu

Matrix Computations and Optimization in Apache Spark

Reza Bosagh Zadeh, Xiangrui Meng, Alexander Ulanov, Burak Yavuz, Li Pu, Shivaram Venkataraman, Evan Sparks, Aaron Staple, Matei Zaharia

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, Machine learning, nVidia, Package, Scala, Spark, Tesla M2050

September 3, 2016 by hgpu

Ultra-Fast Detection of Higher-Order Epistatic Interactions on GPUs

Daniel Junger, Christian Hundt, Jorge Gonzalez-Dominguez, Bertil Schmidt

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Tags: Algorithms, Bioinformatics, Clustering, Computer science, CUDA, Filtering, nVidia, nVidia GeForce GTX Titan X

September 3, 2016 by hgpu

Fast 4D Sheared Filtering for Interactive Rendering of Distribution Effects

Ling-Qi Yan, Soham Uday Mehta, Ravi Ramamoorthi, Fredo Durand

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Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, Filtering, nVidia, nVidia GeForce GTX Titan, Package, Raytracing, Rendering

September 3, 2016 by hgpu

SafeGPU: Contract- and Library-Based GPGPU for Object-Oriented Languages

Alexey Kolesnichenko, Christopher M. Poskitt, Sebastian Nanz

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Tags: Algorithms, Code generation, Computer science, CUDA, nVidia, nVidia Quadro K 2000 M, Package

August 31, 2016 by hgpu

Optimization of RAID Erasure Coding Algorithms for Intel Xeon Phi

Aleksei Marov, Andrey Fedorov

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Tags: Algorithms, Computer science, Error recovery, Intel Xeon Phi

August 31, 2016 by hgpu

Massively parallel simulations of relativistic fluid dynamics on graphics processing units with CUDA

Dennis Bazow, Ulrich W. Heinz, Michael Strickland

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Tags: Algorithms, Computational Physics, CUDA, Fluid dynamics, High Energy Physics - Phenomenology, Intel Xeon Phi, Nuclear Theory, nVidia, nVidia GeForce GTX 560 M, nVidia GeForce GTX 980 Ti, nVidia GeForce GTX Titan Z, Physics

August 28, 2016 by hgpu

Accelerating Exact and Approximate Inference for (Distributed) Discrete Optimization with GPUs

Ferdinando Fioretto, Enrico Pontelli, William Yeoh, Rina Dechter

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Tags: Algorithms, Artificial intelligence, Bayesian, Computer science, nVidia, nVidia GeForce GTX Titan, nVidia GeForce GTX Titan X, Optimization, Package, Tesla M2075

August 23, 2016 by hgpu

Hybrid CPU-GPU Framework for Network Motifs

Ryan A. Rossi, Rong Zhou

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Tags: Algorithms, Computer science, Graph theory, Machine learning, nVidia, nVidia GeForce GTX Titan Black

August 23, 2016 by hgpu

GPU-Acceleration of In-Memory Data Analytics

Evangelia Sitaridi

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Tags: Algorithms, Computer science, CUDA, Databases, nVidia, String matching, Tesla C2070, Tesla K40, Thesis

August 18, 2016 by hgpu

OpenCL + OpenSHMEM Hybrid Programming Model for the Adapteva Epiphany Architecture

David Richie, James Ross

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Tags: Algorithms, ARM, Computer science, Matrix multiplication, Memory model, OpenCL, Package

August 16, 2016 by hgpu

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

Data-efficient LLM Fine-tuning for Code Generation

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

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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high performance computing on graphics processing units: hgpu.org

Programming

Implementation and Performance Analysis of Many-body Quantum Chemical Methods on the Intel Xeon Phi Coprocessor and NVIDIA GPU Accelerator

Matrix Computations and Optimization in Apache Spark

Ultra-Fast Detection of Higher-Order Epistatic Interactions on GPUs

Fast 4D Sheared Filtering for Interactive Rendering of Distribution Effects

SafeGPU: Contract- and Library-Based GPGPU for Object-Oriented Languages

Optimization of RAID Erasure Coding Algorithms for Intel Xeon Phi

Massively parallel simulations of relativistic fluid dynamics on graphics processing units with CUDA

Accelerating Exact and Approximate Inference for (Distributed) Discrete Optimization with GPUs

Hybrid CPU-GPU Framework for Network Motifs

GPU-Acceleration of In-Memory Data Analytics

OpenCL + OpenSHMEM Hybrid Programming Model for the Adapteva Epiphany Architecture

Recent source codes

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

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

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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