This course will help prepare students for developing code that can process large amounts of data in parallel on Graphics Processing Units (GPUs). It will learn on how to implement software that can solve complex problems with the leading consumer to enterprise-grade GPUs available using Nvidia CUDA. They will focus on the hardware and software capabilities, including the use of 100s to 1000s of threads and various forms of memory.
提供方
GPU Programming 專項課程约翰霍普金斯大学課程信息
8,119 次近期查看
可靈活調整截止日期
根據您的日程表重置截止日期。
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 2 門課程（共 4 門）
中級
Some experience in C/C++ programming
完成時間大約為22 小時
英語（English）
字幕：英語（English）
對員工進行熱門技能培訓能否為您的公司帶來益處？
體驗 Coursera 企業版您將學到的內容有
Students will learn how to utilize the CUDA framework to write C/C++ software that runs on CPUs and Nvidia GPUs.
Students will transform sequential CPU algorithms and programs into CUDA kernels that execute 100s to 1000s of times simultaneously on GPU hardware.
您將獲得的技能
- Cuda
- Algorithms
- C/C++
- GPU
- Nvidia
可靈活調整截止日期
根據您的日程表重置截止日期。
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 2 門課程（共 4 門）
中級
Some experience in C/C++ programming
完成時間大約為22 小時
英語（English）
字幕：英語（English）
對員工進行熱門技能培訓能否為您的公司帶來益處？
體驗 Coursera 企業版
提供方
约翰霍普金斯大学
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
授課大綱 - 您將從這門課程中學到什麼
完成時間為 4 小時
Course Overview
The purpose of this module is for students to understand how the course will be run, topics, how they will be assessed, and expectations.
完成時間為 4 小時
3 個視頻 （總計 11 分鐘）, 4 個閱讀材料, 1 個測驗
完成時間為 5 小時
Threads, Blocks and Grids
The single most important concept for using GPUs to solve complex and large-scale problems, is management of threads. CUDA provides two- and three-dimensional logical abstractions of threads, blocks and grids. Students will develop programs that utilize threads, blocks, and grids to process large 2 to 3-dimensional data sets.
完成時間為 5 小時
8 個視頻 （總計 50 分鐘）, 1 個閱讀材料, 4 個測驗
完成時間為 5 小時
Host and Global Memory
To manage the access and modification of data in physical memory effectively, students will need to load data into CPU (host) and GPU (global) general-purpose memory. Students will create software that allocates host memory and transfers it into global memory for use by threads. Students will also learn the capabilities and speeds of these types of memories.
完成時間為 5 小時
8 個視頻 （總計 23 分鐘）
完成時間為 4 小時
Shared and Constant Memory
To improve performance in GPU software, students will need to utilized mutable (shared) and static (constant) memory. They will use them to apply masks to all items of a data set, to manage the communication between threads, and use for caching in complex programs.
完成時間為 4 小時
6 個視頻 （總計 22 分鐘）
關於 GPU Programming 專項課程
This specialization is intended for data scientists and software developers to create software that uses commonly available hardware. Students will be introduced to CUDA and libraries that allow for performing numerous computations in parallel and rapidly. Applications for these skills are machine learning, image/audio signal processing, and data processing.
常見問題
我什么时候能够访问课程视频和作业？
我订阅此专项课程后会得到什么？
有助学金吗？
Can I program on my own desktop/laptop?
還有其他問題嗎？請訪問 學生幫助中心。
