GPGPU microprocessor architecture
-
Updated
Apr 26, 2024 - C
GPGPU microprocessor architecture
CUDA by Example, written by two senior members of the CUDA software platform team, shows programmers how to employ this new technology. The authors introduce each area of CUDA development through working examples.
qCUDA: GPGPU Virtualization at a New API Remoting Method with Para-virtualization
A versatile multifluid HD/MHD code that runs on clusters of CPUs or GPUs, with special emphasis on protoplanetary disks.
PaRSEC is a generic framework for architecture aware scheduling and management of micro-tasks on distributed, GPU accelerated, many-core heterogeneous architectures. PaRSEC assigns computation threads to the cores, GPU accelerators, overlaps communications and computations and uses a dynamic, fully-distributed scheduler based on architectural fe…
CUDA bindings for Ruby
DPLASMA is a highly optimized, accelerator-aware, implementation of a dense linear algebra package for distributed heterogeneous systems. It is designed to deliver sustained performance for distributed systems where each node featuring multiple sockets of multicore processors, and if available, accelerators, using the PaRSEC runtime as a backend.
How fast can we brute force a 64-bit comparison?
CUDA implementation of a Direct Simulation Monte Carlo method for gas dynamics
This serves as a repository for reproducibility of the SC21 paper "In-Depth Analyses of Unified Virtual Memory System for GPU Accelerated Computing," as well as several components of the IPDPS21 paper "Demystifying GPU UVM Cost with Deep Runtime and Workload Analysis."
C implementation for CPU and GPU of OpenSimplex 2
Just a bunch of methods and scripts to test performances within containers
Implementation of an Image Processing Library for time consuming operations such as Image Blurring,Negation,Edge Detection and Contrast Stretching.
best CPU/GPU sparse solver for large sparse matrices
PHP extension for efficient scientific computing and array manipulation with GPU support
This serves as reproducibility for "Slate: Enabling Workload-Aware Efficient Multiprocessing for Modern GPGPUs" at IPDPS 2019.
Fast Fourier Transform using the Vulkan API
Add a description, image, and links to the gpu-computing topic page so that developers can more easily learn about it.
To associate your repository with the gpu-computing topic, visit your repo's landing page and select "manage topics."