high performance computing on graphics processing units: hgpu.org

Karthikeyan Vaidyanathan, Kiran Pamnany, Dhiraj D. Kalamkar, Alexander Heinecke, Mikhail Smelyanskiy, Jongsoo Park, Daehyun Kim, Aniruddha Shet G, Bharat Kaul, Balint Joo, Pradeep Dubey

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Tags: Cluster computing, Computer science, FFT, Intel Xeon Phi, Performance, QCD

August 15, 2014 by hgpu

Design and Evaluation of Scalable Concurrent Queues for Many-Core Architectures

Thomas R. W. Scogland, Wu-chun Feng

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Tags: ATI, ATI Radeon HD 7970, Computer science, Intel Xeon Phi, nVidia, nVidia GeForce GTX 280, OpenCL, Performance, Tesla K20

August 15, 2014 by hgpu

Energy Evaluation for Applications with Different Thread Affinities on the Intel Xeon Phi

Gary Lawson, Masha Sosonkina, Yuzhong Shen

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Tags: Computer science, Energy-efficient computing, Intel Xeon Phi, Performance

August 15, 2014 by hgpu

GPU-SPARC: Accelerating Parallelism in Multi-GPU Real-Time Systems

Wookhyun Han, Hwidong Bae, Hyosu Kim, Jiyoen Lee, Insik Shin

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Tags: Computer science, nVidia, nVidia GeForce GTX 560 Ti, OpenCL, Package, Performance

August 13, 2014 by hgpu

Optimising Purely Functional GPU Programs (Thesis)

Trevor L. McDonell

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Tags: Code generation, Computer science, CUDA, nVidia, Package, Performance, Tesla T10, Thesis

August 10, 2014 by hgpu

Direct Numerical Simulation of Turbulent Flows with Parallel Algorithms for Various Computing Architectures

A. Gorobets, F.X. Trias, R. Borrell, G. Oyarzun, A. Oliva

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Tags: ATI, ATI Radeon HD 5870, ATI Radeon HD 6970, ATI Radeon HD 7970, Fluid dynamics, Intel Xeon Phi, MPI, Numerical simulation, nVidia, nVidia GeForce GTX 470, nVidia GeForce GTX Titan, OpenCL, OpenMP, Performance, Tesla C1060, Tesla C2050

August 10, 2014 by hgpu

Optimizing performance per watt on GPUs in High Performance Computing: temperature, frequency and voltage effects

D. C. Price, M. A. Clark, B. R. Barsdell, R. Babich, L. J. Greenhill

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Tags: Astrophysics, CUDA, Energy-efficient computing, Instrumentation and Methods for Astrophysics, nVidia, nVidia GeForce GTX 780, Package, Performance, Signal processing, Tesla K20

August 1, 2014 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

Optimistic Parallelism on GPUs

Bandwidth Requirements of GPU Architectures

Estimating GPU Speedups for Programs Without Writing a Single Line of GPU Code

High Level Programming for Heterogeneous Architectures

Practical Symbolic Race Checking of GPU Programs

Improving Communication Performance and Scalability of Native Applications on Intel Xeon Phi Coprocessor Clusters

Design and Evaluation of Scalable Concurrent Queues for Many-Core Architectures

Energy Evaluation for Applications with Different Thread Affinities on the Intel Xeon Phi

GPU-SPARC: Accelerating Parallelism in Multi-GPU Real-Time Systems

Optimising Purely Functional GPU Programs (Thesis)

Direct Numerical Simulation of Turbulent Flows with Parallel Algorithms for Various Computing Architectures

Optimizing performance per watt on GPUs in High Performance Computing: temperature, frequency and voltage effects

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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