CSR: Small: Effective Data Compression for Modern Memory Systems

CSR：小：现代内存系统的有效数据压缩

• 批准号: 1423172
• 负责人: Todd Mowry
• 金额: $ 49.76万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-10-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1423172&HistoricalAwards=false
• 关键词: CSR; Small; Effective; Data; Compression

Many areas of science and technology have experienced transformational changes in recent years due to the increased ability to collect and analyze extremely large data sets. As a result, computers are often limited by their ability to move data in and out of their processors (or CPUs). One attractive approach to reducing this performance bottleneck would be to compress the data throughout the computer's memory system. Although data compression has been successful in certain areas of computing (e.g., shrinking the size of files), it has not been widely adopted within modern memory systems. This research is to develop a new memory compression technique that is fast enough, simple enough, and effective enough (in saving space) to make memory compression practical and attractive.The key insight to the proposed approach is that decompression latency and simplicity of design are far more critical than compression ratio when designing a compression scheme that is effective for modern memory. Preliminary work has identified simple and effective mechanisms for compressing data in on-chip caches and in main memory that achieve significant compression ratios while adding minimal access latency overhead. The simplicity of the proposed mechanisms also enables elegant solutions for dealing with the practical challenges of how on-chip caches and main memories are organized in modern systems. The anticipated data compression framework will help improve the performance, cost, and energy efficiency of a wide spectrum of computer systems, ranging from high-end servers (including Cloud Computing, high-performance computing, and other distributed systems) down to mobile and embedded devices.The planned compression framework should relieve pressure on both the capacity of the various layers of the memory hierarchy (including caches, DRAM, non-volatile memory technologies, etc.) as well as the bandwidth of the interconnects that transfer data between these layers. This in turn would allow systems designers to avoid over-provisioning these resources. As a result, future computer systems should be better suited to the increasingly data-intensive workloads of the future.

近年来，由于收集和分析超大数据集的能力不断增强，许多科学技术领域都经历了变革。 因此，计算机通常受到将数据移入和移出处理器（或 CPU）的能力的限制。 减少这一性能瓶颈的一种有吸引力的方法是压缩整个计算机内存系统的数据。 尽管数据压缩在某些计算领域取得了成功（例如缩小文件的大小），但它尚未在现代内存系统中广泛采用。 这项研究旨在开发一种新的内存压缩技术，该技术足够快、足够简单、足够有效（节省空间），使内存压缩实用且有吸引力。该方法的关键见解是解压缩延迟和设计简单性在设计对现代内存有效的压缩方案时，比压缩比更重要。初步工作已经确定了简单有效的机制，用于压缩片上缓存和主存储器中的数据，从而实现显着的压缩比，同时增加最小的访问延迟开销。所提出的机制的简单性还使得优雅的解决方案能够应对现代系统中片上缓存和主存储器如何组织的实际挑战。 预期的数据压缩框架将有助于提高各种计算机系统的性能、成本和能源效率，从高端服务器（包括云计算、高性能计算和其他分布式系统）到移动和嵌入式系统计划的压缩框架应该减轻存储器层次结构各层（包括高速缓存、DRAM、非易失性存储器技术等）的容量以及在这些层之间传输数据的互连带宽的压力。这反过来又将使系统设计人员能够避免过度配置这些资源。因此，未来的计算机系统应该更适合未来日益增长的数据密集型工作负载。