R. Hameed, W. Qadeer, Megan Wachs, Omid Azizi, A. Solomatnikov, Benjamin C. Lee, S. Richardson, C. Kozyrakis, M. Horowitz
{"title":"了解通用芯片效率低下的根源","authors":"R. Hameed, W. Qadeer, Megan Wachs, Omid Azizi, A. Solomatnikov, Benjamin C. Lee, S. Richardson, C. Kozyrakis, M. Horowitz","doi":"10.1145/1815961.1815968","DOIUrl":null,"url":null,"abstract":"Due to their high volume, general-purpose processors, and now chip multiprocessors (CMPs), are much more cost effective than ASICs, but lag significantly in terms of performance and energy efficiency. This paper explores the sources of these performance and energy overheads in general-purpose processing systems by quantifying the overheads of a 720p HD H.264 encoder running on a general-purpose CMP system. It then explores methods to eliminate these overheads by transforming the CPU into a specialized system for H.264 encoding. We evaluate the gains from customizations useful to broad classes of algorithms, such as SIMD units, as well as those specific to particular computation, such as customized storage and functional units. The ASIC is 500x more energy efficient than our original four-processor CMP. Broadly applicable optimizations improve performance by 10x and energy by 7x. However, the very low energy costs of actual core ops (100s fJ in 90nm) mean that over 90% of the energy used in these solutions is still \"overhead\". Achieving ASIC-like performance and efficiency requires algorithm-specific optimizations. For each sub-algorithm of H.264, we create a large, specialized functional unit that is capable of executing 100s of operations per instruction. This improves performance and energy by an additional 25x and the final customized CMP matches an ASIC solution's performance within 3x of its energy and within comparable area.","PeriodicalId":132033,"journal":{"name":"Proceedings of the 37th annual international symposium on Computer architecture","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"479","resultStr":"{\"title\":\"Understanding sources of inefficiency in general-purpose chips\",\"authors\":\"R. Hameed, W. Qadeer, Megan Wachs, Omid Azizi, A. Solomatnikov, Benjamin C. Lee, S. Richardson, C. Kozyrakis, M. Horowitz\",\"doi\":\"10.1145/1815961.1815968\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Due to their high volume, general-purpose processors, and now chip multiprocessors (CMPs), are much more cost effective than ASICs, but lag significantly in terms of performance and energy efficiency. This paper explores the sources of these performance and energy overheads in general-purpose processing systems by quantifying the overheads of a 720p HD H.264 encoder running on a general-purpose CMP system. It then explores methods to eliminate these overheads by transforming the CPU into a specialized system for H.264 encoding. We evaluate the gains from customizations useful to broad classes of algorithms, such as SIMD units, as well as those specific to particular computation, such as customized storage and functional units. The ASIC is 500x more energy efficient than our original four-processor CMP. Broadly applicable optimizations improve performance by 10x and energy by 7x. However, the very low energy costs of actual core ops (100s fJ in 90nm) mean that over 90% of the energy used in these solutions is still \\\"overhead\\\". Achieving ASIC-like performance and efficiency requires algorithm-specific optimizations. For each sub-algorithm of H.264, we create a large, specialized functional unit that is capable of executing 100s of operations per instruction. This improves performance and energy by an additional 25x and the final customized CMP matches an ASIC solution's performance within 3x of its energy and within comparable area.\",\"PeriodicalId\":132033,\"journal\":{\"name\":\"Proceedings of the 37th annual international symposium on Computer architecture\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-06-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"479\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 37th annual international symposium on Computer architecture\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1815961.1815968\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 37th annual international symposium on Computer architecture","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1815961.1815968","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 479
摘要
由于它们的高容量,通用处理器和现在的芯片多处理器(cmp)比asic更具成本效益,但在性能和能效方面明显滞后。本文通过量化在通用CMP系统上运行的720p HD H.264编码器的开销,探讨了通用处理系统中这些性能和能源开销的来源。然后探讨了通过将CPU转换为H.264编码的专用系统来消除这些开销的方法。我们评估了从定制中获得的收益,这些定制对广泛的算法类有用,比如SIMD单元,以及那些特定于特定计算的算法,比如定制存储和功能单元。ASIC比我们原来的四处理器CMP节能500倍。广泛适用的优化将性能提高10倍,将能耗提高7倍。然而,实际核心操作的能量成本非常低(90nm的100 fJ),这意味着这些解决方案中使用的90%以上的能量仍然是“开销”。实现类似asic的性能和效率需要特定于算法的优化。对于H.264的每个子算法,我们创建了一个大型的专用功能单元,每个指令能够执行100次操作。这将性能和能量提高了25倍,最终定制的CMP在3倍的能量和可比区域内与ASIC解决方案的性能相匹配。
Understanding sources of inefficiency in general-purpose chips
Due to their high volume, general-purpose processors, and now chip multiprocessors (CMPs), are much more cost effective than ASICs, but lag significantly in terms of performance and energy efficiency. This paper explores the sources of these performance and energy overheads in general-purpose processing systems by quantifying the overheads of a 720p HD H.264 encoder running on a general-purpose CMP system. It then explores methods to eliminate these overheads by transforming the CPU into a specialized system for H.264 encoding. We evaluate the gains from customizations useful to broad classes of algorithms, such as SIMD units, as well as those specific to particular computation, such as customized storage and functional units. The ASIC is 500x more energy efficient than our original four-processor CMP. Broadly applicable optimizations improve performance by 10x and energy by 7x. However, the very low energy costs of actual core ops (100s fJ in 90nm) mean that over 90% of the energy used in these solutions is still "overhead". Achieving ASIC-like performance and efficiency requires algorithm-specific optimizations. For each sub-algorithm of H.264, we create a large, specialized functional unit that is capable of executing 100s of operations per instruction. This improves performance and energy by an additional 25x and the final customized CMP matches an ASIC solution's performance within 3x of its energy and within comparable area.