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Programming Massively Parallel Processors with CUDA

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When? This feed was archived on April 20, 2016 12:41 (8+ y ago). Last successful fetch was on April 21, 2016 12:43 (8+ y ago)

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コンテンツは Stanford University によって提供されます。エピソード、グラフィック、ポッドキャストの説明を含むすべてのポッドキャスト コンテンツは、Stanford University またはそのポッドキャスト プラットフォーム パートナーによって直接アップロードされ、提供されます。誰かがあなたの著作物をあなたの許可なく使用していると思われる場合は、ここで概説されているプロセスに従うことができますhttps://ja.player.fm/legal
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.
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Artwork

Programming Massively Parallel Processors with CUDA

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アーカイブされたシリーズ ("無効なフィード" status)

When? This feed was archived on April 20, 2016 12:41 (8+ y ago). Last successful fetch was on April 21, 2016 12:43 (8+ y ago)

Why? 無効なフィード status. サーバーは持続期間に有効なポッドキャストのフィードを取得することができませんでした。

What now? You might be able to find a more up-to-date version using the search function. This series will no longer be checked for updates. If you believe this to be in error, please check if the publisher's feed link below is valid and contact support to request the feed be restored or if you have any other concerns about this.

Manage series 14027
コンテンツは Stanford University によって提供されます。エピソード、グラフィック、ポッドキャストの説明を含むすべてのポッドキャスト コンテンツは、Stanford University またはそのポッドキャスト プラットフォーム パートナーによって直接アップロードされ、提供されます。誰かがあなたの著作物をあなたの許可なく使用していると思われる場合は、ここで概説されているプロセスに従うことができますhttps://ja.player.fm/legal
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.
  continue reading

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