CSR: Small: Effective Data Compression for Modern Memory Systems

近年来，由于收集和分析超大数据集的能力增强，许多科学和技术领域都经历了转型变化。因此，计算机通常受到将数据移入和移出其处理器（或cpu）的能力的限制。减少这种性能瓶颈的一个有吸引力的方法是压缩整个计算机内存系统中的数据。虽然数据压缩在某些计算领域（例如，缩小文件大小）取得了成功，但在现代存储系统中还没有被广泛采用。这项研究是为了开发一种新的内存压缩技术，它足够快，足够简单，足够有效（在节省空间方面），使内存压缩具有实用性和吸引力。所提出方法的关键见解是，在设计对现代存储器有效的压缩方案时，解压缩延迟和设计的简单性远比压缩比重要。初步工作已经确定了在片上缓存和主存储器中压缩数据的简单而有效的机制，这些机制在增加最小访问延迟开销的同时实现了显著的压缩比。所提出的机制的简单性也为处理在现代系统中如何组织片上缓存和主存储器的实际挑战提供了优雅的解决方案。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，范围从高端服务器（包括云计算、高性能计算和其他分布式系统）到移动和嵌入式设备。计划的压缩框架应该减轻对存储器层次结构的各个层（包括缓存、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。这反过来又允许系统设计人员避免过度配置这些资源。因此，未来的计算机系统应该能够更好地适应未来日益增长的数据密集型工作负载。

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.