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Programming Many-Core Chips

Andras Vajda

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Tags: Book, Cluster computing, Computer science, CUDA, MPI, nVidia, OpenCL, OpenMP, Programming techniques

September 8, 2011 by hgpu

Automatic CPU-GPU communication management and optimization

Thomas B. Jablin, Prakash Prabhu, James A. Jablin, Nick P. Johnson, Stephen R. Beard, David I. August

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Optimization, Performance, Programming techniques

September 7, 2011 by hgpu

High performance computation and interactive display of molecular orbitals on GPUs and multi-core CPUs

John E. Stone, Jan Saam, David J. Hardy, Kirby L. Vandivort, Wen-mei W. Hwu, Klaus Schulten

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Tags: Algorithms, Chemistry, CUDA, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, Performance, Programming techniques, Rendering, Visualization

September 7, 2011 by hgpu

Bridging parallel and reconfigurable computing with multilevel PGAS and SHMEM+

V. Aggarwal, A. George, K. Yalamanchili, C. Yoon, H. Lam, G. Stitt

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Tags: Computer science, FPGA, Heterogeneous systems, Performance, Programming Languages, Programming techniques

September 6, 2011 by hgpu

The impact of diverse memory architectures on multicore consumer software: an industrial perspective from the video games domain

George Russel, Colin Riley, Neil Henning, Uwe Dolinsky, Andrew Richards, Alastair F. Donaldson, Alexander S. van Amesfoort

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Tags: Cell processor, Computer science, Memory, Performance, Programming techniques

August 28, 2011 by hgpu

A new multi-core pipelined architecture for executing sequential programs for parallel geospatial computing

Duoduo Liao, Simon Y. Berkovich

Tags: Computer science, Programming techniques

August 28, 2011 by hgpu

A breadth-first course in multicore and manycore programming

Suzanne Rivoire

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Tags: Computer science, CUDA, High-level Languages, nVidia, OpenMP, Programming techniques

August 27, 2011 by hgpu

PFunc: modern task parallelism for modern high performance computing

Prabhanjan Kambadur, Anshul Gupta, Amol Ghoting, Haim Avron, Andrew Lumsdaine

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Tags: Computer science, Package, Performance, Programming techniques, Software Engineering

August 26, 2011 by hgpu

JCudaMP: OpenMP/Java on CUDA

Georg Dotzler, Ronald Veldema, Michael Klemm

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Tags: Code generation, Computer science, CUDA, Java, nVidia, nVidia GeForce GTX 280, OpenMP, Programming Languages, Programming techniques

August 23, 2011 by hgpu

MDR: performance model driven runtime for heterogeneous parallel platforms

Jacques A. Pienaar, Anand Raghunathan, Srimat Chakradhar

Tags: Computer science, Heterogeneous systems, Performance, Programming techniques, Task scheduling

August 23, 2011 by hgpu

High performance content-based matching using GPUs

Alessandro Margara, Gianpaolo Cugola

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Tags: Algorithms, Computer science, CUDA, Information Retrieval, nVidia, nVidia GeForce GTX 460, Package, Performance, Programming techniques

August 23, 2011 by hgpu

Workload and network-optimized computing systems

D. P. LaPotin, S. Daijavad, C. L. Johnson, S. W. Hunter, K. Ishizaki, H. Franke, H. D. Achilles, D. P. Dumarot, N. A. Greco, B. Davari

Tags: Computer science, Performance, Programming techniques

August 23, 2011 by hgpu

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming Many-Core Chips

Automatic CPU-GPU communication management and optimization

High performance computation and interactive display of molecular orbitals on GPUs and multi-core CPUs

Bridging parallel and reconfigurable computing with multilevel PGAS and SHMEM+

The impact of diverse memory architectures on multicore consumer software: an industrial perspective from the video games domain

A new multi-core pipelined architecture for executing sequential programs for parallel geospatial computing

A breadth-first course in multicore and manycore programming

PFunc: modern task parallelism for modern high performance computing

JCudaMP: OpenMP/Java on CUDA

MDR: performance model driven runtime for heterogeneous parallel platforms

High performance content-based matching using GPUs

Workload and network-optimized computing systems

Recent source codes

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

ROCm's implementation of Gromacs

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

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