iTunes

Открытие iTunes Store.Если iTunes не открывается, нажмите значок iTunes на рабочем столе Windows или панели Dock.Индикатор выполнения
Открытие Apple Books.Если приложение Apple Books не открывается, нажмите на значок приложения на панели Dock.Индикатор выполнения
iTunes

iTunes — самый простой в мире способ систематизации и добавления цифровых медиафайлов в коллекцию.

Не удалось обнаружить iTunes на Вашем компьютере. Для загрузки и подписки на Programming Massively Parallel Processors with CUDA исполнителя Stanford University установите iTunes прямо сейчас.

Уже есть iTunes? Нажмите «У меня есть iTunes», чтобы открыть продукт прямо сейчас.

У меня есть iTunes Загрузить бесплатно

Programming Massively Parallel Processors with CUDA

От Stanford University

Для прослушивания аудиоподкаста наведите курсор на название и нажмите «Воспроизвести». Откройте iTunes для загрузки подкастов и подписки на них.

Описание

Virtually all semiconductor market domains, including PCs, game consoles, mobile handsets, servers, supercomputers, and networks, are converging to concurrent platforms. There are two important reasons for this trend. First, these concurrent processors can potentially offer more effective use of chip space and power than traditional monolithic microprocessors for many demanding applications. Second, an increasing number of applications that traditionally used Application Specific Integrated Circuits (ASICs) are now implemented with concurrent processors in order to improve functionality and reduce engineering cost. The real challenge is to develop applications software that effectively uses these concurrent processors to achieve efficiency and performance goals. The aim of this course is to provide students with knowledge and hands-on experience in developing applications software for processors with massively parallel computing resources. In general, we refer to a processor as massively parallel if it has the ability to complete more than 64 arithmetic operations per clock cycle. Many commercial offerings from NVIDIA, AMD, and Intel already offer such levels of concurrency. Effectively programming these processors will require in-depth knowledge about parallel programming principles, as well as the parallelism models, communication models, and resource limitations of these processors. The target audiences of the course are students who want to develop exciting applications for these processors, as well as those who want to develop programming tools and future implementations for these processors. Visit the CS193G companion website for course materials.