Understanding and Improving the Latency of DRAM-Based Memory Systems

Over the past two decades, the storage capacity and access bandwidth of main memory have improved tremendously, by 128x and 20x, respectively. These improvements are mainly due to the continuous technology scaling of DRAM (dynamic random-access memory), which has been used as the physical substrate for main memory. In stark contrast with capacity and bandwidth, DRAM latency has remained almost constant, reducing by only 1.3x in the same time frame. Therefore, long DRAM latency continues to be a critical performance bottleneck in modern systems. Increasing core counts, and the emergence of increasingly more data-intensive and latency-critical applications further stress the importance of providing low-latency memory access. In this dissertation, we identify three main problems that contribute significantly to long latency of DRAM accesses. To address these problems, we present a series of new techniques. Our new techniques significantly improve both system performance and energy efficiency. We also examine the critical relationship between supply voltage and latency in modern DRAM chips and develop new mechanisms that exploit this voltage-latency trade-off to improve energy efficiency. The key conclusion of this dissertation is that augmenting DRAM architecture with simple and low-cost features, and developing a better understanding of manufactured DRAM chips together lead to significant memory latency reduction as well as energy efficiency improvement. We hope and believe that the proposed architectural techniques and the detailed experimental data and observations on real commodity DRAM chips presented in this dissertation will enable development of other new mechanisms to improve the performance, energy efficiency, or reliability of future memory systems.Comment: PhD Dissertatio

FlatPack: Flexible Compaction of Compressed Memory

The capacity and bandwidth of main memory is an increasingly important factor in computer system performance. Memory compression and compaction have been combined to increase effective capacity and reduce costly page faults. However, existing systems typically maintain compaction at the expense of bandwidth. One major cause of extra traffic in such systems is page overflows, which occur when data compressibility degrades and compressed pages must be reorganized. This paper introduces FlatPack, a novel approach to memory compaction which is able to mitigate this overhead by reorganizing compressed data dynamically with less data movement. Reorganization is carried out by an addition to the memory controller, without intervention from software. FlatPack is able to maintain memory capacity competitive with current state-of-the-art memory compression designs, while reducing mean memory traffic by up to 67%. This yields average improvements in performance and total system energy consumption over existing memory compression solutions of 31-46% and 11-25%, respectively. In total, FlatPack improves on baseline performance and energy consumption by 108% and 40%, respectively, in a single-core system, and 83% and 23%, respectively, in a multi-core system

Software caching techniques and hardware optimizations for on-chip local memories

Despite the fact that the most viable L1 memories in processors are caches, on-chip local memories have been a great topic of consideration lately. Local memories are an interesting design option due to their many benefits: less area occupancy, reduced energy consumption and fast and constant access time. These benefits are especially interesting for the design of modern multicore processors since power and latency are important assets in computer architecture today. Also, local memories do not generate coherency traffic which is important for the scalability of the multicore systems. Unfortunately, local memories have not been well accepted in modern processors yet, mainly due to their poor programmability. Systems with on-chip local memories do not have hardware support for transparent data transfers between local and global memories, and thus ease of programming is one of the main impediments for the broad acceptance of those systems. This thesis addresses software and hardware optimizations regarding the programmability, and the usage of the on-chip local memories in the context of both single-core and multicore systems. Software optimizations are related to the software caching techniques. Software cache is a robust approach to provide the user with a transparent view of the memory architecture; but this software approach can suffer from poor performance. In this thesis, we start optimizing traditional software cache by proposing a hierarchical, hybrid software-cache architecture. Afterwards, we develop few optimizations in order to speedup our hybrid software cache as much as possible. As the result of the software optimizations we obtain that our hybrid software cache performs from 4 to 10 times faster than traditional software cache on a set of NAS parallel benchmarks. We do not stop with software caching. We cover some other aspects of the architectures with on-chip local memories, such as the quality of the generated code and its correspondence with the quality of the buffer management in local memories, in order to improve performance of these architectures. Therefore, we run our research till we reach the limit in software and start proposing optimizations on the hardware level. Two hardware proposals are presented in this thesis. One is about relaxing alignment constraints imposed in the architectures with on-chip local memories and the other proposal is about accelerating the management of local memories by providing hardware support for the majority of actions performed in our software cache.Malgrat les memòries cau encara son el component basic pel disseny del subsistema de memòria, les memòries locals han esdevingut una alternativa degut a les seves característiques pel que fa a l’ocupació d’àrea, el seu consum energètic i el seu rendiment amb un temps d’accés ràpid i constant. Aquestes característiques son d’especial interès quan les properes arquitectures multi-nucli estan limitades pel consum de potencia i la latència del subsistema de memòria.Les memòries locals pateixen de limitacions respecte la complexitat en la seva programació, fet que dificulta la seva introducció en arquitectures multi-nucli, tot i els avantatges esmentats anteriorment. Aquesta tesi presenta un seguit de solucions basades en programari i maquinari específicament dissenyat per resoldre aquestes limitacions.Les optimitzacions del programari estan basades amb tècniques d'emmagatzematge de memòria cau suportades per llibreries especifiques. La memòria cau per programari és un sòlid mètode per proporcionar a l'usuari una visió transparent de l'arquitectura, però aquest enfocament pot patir d'un rendiment deficient. En aquesta tesi, es proposa una estructura jeràrquica i híbrida. Posteriorment, desenvolupem optimitzacions per tal d'accelerar l’execució del programari que suporta el disseny de la memòria cau. Com a resultat de les optimitzacions realitzades, obtenim que el nostre disseny híbrid es comporta de 4 a 10 vegades més ràpid que una implementació tradicional de memòria cau sobre un conjunt d’aplicacions de referencia, com son els “NAS parallel benchmarks”.El treball de tesi inclou altres aspectes de les arquitectures amb memòries locals, com ara la qualitat del codi generat i la seva correspondència amb la qualitat de la gestió de memòria intermèdia en les memòries locals, per tal de millorar el rendiment d'aquestes arquitectures. La tesi desenvolupa propostes basades estrictament en el disseny de nou maquinari per tal de millorar el rendiment de les memòries locals quan ja no es possible realitzar mes optimitzacions en el programari. En particular, la tesi presenta dues propostes de maquinari: una relaxa les restriccions imposades per les memòries locals respecte l’alineament de dades, l’altra introdueix maquinari específic per accelerar les operacions mes usuals sobre les memòries locals