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1. www.cs.purdue.edu › homes › aliagaThe Way of the GPU - Purdue University

   www.cs.purdue.edu › homes › aliaga

   Concepts to GPUs • GPUs are designed for graphics – Highly parallel tasks • GPUs process independent vertices & fragments – Temporary registers are zeroed – No shared or static data – No read-modify-write buffers • Data-parallel processing – GPUs architecture is ALU-heavy

2. www.cs.purdue.edu › academic-programs › coursesCS59200CGP Compilers for GPUs - Purdue University

   www.cs.purdue.edu › academic-programs › courses

   Students will analyze the open-source OpenCL compiler tool kit (in the framework of LLVM) and understand. the techniques implemented in such a tool kit. how such techniques are designed to exploit GPU hardware. alternative ways to write CUDA programs and how they may impact on the performance.

3. engineering.purdue.edu › ~smidkiff › KKUModule 02 - Introduction to CUDA C - engineering.purdue.edu

   engineering.purdue.edu › ~smidkiff › KKU

   Ease of use: Using libraries enables GPU acceleration without in-depth knowledge of GPU programming “Drop-in”: Many GPU-accelerated libraries follow standard APIs, thus enabling acceleration with minimal code changes Quality: Libraries offer high-quality implementations of functions encountered in a broad range of applications

4. engineering.purdue.edu › online › coursesProgrammable Accelerator Architectures - Purdue University

   engineering.purdue.edu › online › courses

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   Understand the design of contemporary programmable accelerators (i.e. GPUs) and how the programming model relates to the design. Program a GPU using CUDA to perform data parallel tasks and perform performance optimizations on their code, leveraging knowledge of the architectures design.

5. engineering.purdue.edu › ~smidkiff › ece563Lecture 1.1 – Course Introduction - Purdue University

   engineering.purdue.edu › ~smidkiff › ece563

   Learn how to program heterogeneous parallel computing systems and achieve. High performance and energy-efficiency. Functionality and maintainability. Scalability across future generations. Portability across vendor devices. Technical subjects. Parallel programming API, tools and techniques.

6. cuda-tutorial.readthedocs.io › en › latestTutorial 01: Say Hello to CUDA - CUDA Tutorial - Read the Docs

   cuda-tutorial.readthedocs.io › en › latest

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   This tutorial is an introduction for writing your first CUDA C program and offload computation to a GPU. We will use CUDA runtime API throughout this tutorial. CUDA is a platform and programming model for CUDA-enabled GPUs. The platform exposes GPUs for general purpose computing.

7. www.cs.purdue.edu › academic-programs › coursesCS 240: Programming in C - Purdue University

   www.cs.purdue.edu › academic-programs › courses

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   25 kwi 2017 · CS 240: Programming in C. Prerequisite: CS 18000 (Problem Solving and Object-Oriented Programming) Detailed Syllabus. UNIX command environment, the basic C development cycle