The computing world today is in the middle of a revolution: mobile clients and cloud computing have emerged as the dominant paradigms driving programming and hardware innovation today. The Fifth Edition of Computer Architecture focuses on this dramatic shift, exploring the ways in which software and technology in the "cloud" are accessed by cell phones, tablets, laptops, and other mobile computing devices. Each chapter includes two real-world examples, one mobile and one datacenter, to illustrate this revolutionary change. Updated to cover the mobile computing revolutionEmphasizes the two most important topics in architecture today: memory hierarchy and parallelism in all its forms.Develops common themes throughout each chapter: power, performance, cost, dependability, protection, programming models, and emerging trends ("What's Next")Includes three review appendices in the printed text. Additional reference appendices are available online.Includes updated Case Studies and completely new exercises.

Computer Architecture, Fifth Edition: A Quantitative Approach

Is your memory hierarchy stopping your microprocessor from performing at the high level it should be? Memory Systems: Cache, DRAM, Disk shows you how to resolve this problem. The book tells you everything you need to know about the logical design and operation, physical design and operation, performance characteristics and resulting design trade-offs, and the energy consumption of modern memory hierarchies. You learn how to to tackle the challenging optimization problems that result from the side-effects that can appear at any point in the entire hierarchy.As a result you will be able to design and emulate the entire memory hierarchy. . Understand all levels of the system hierarchy -Xcache, DRAM, and disk. . Evaluate the system-level effects of all design choices. . Model performance and energy consumption for each component in the memory hierarchy.

Memory Systems: Cache, DRAM, Disk

Prefetching is one approach to reducing the latency of memory operations in modern computer systems. In this paper, we describe the Markov prefetcher. This prefetcher acts as an interface between the on-chip and off-chip cache, and can be added to existing computer designs. The Markov prefetcher is distinguished by prefetching multiple reference predictions from the memory subsystem, and then prioritizing the delivery of those references to the processor.This design results in a prefetching system that provides good coverage, is accurate and produces timely results that can be effectively used by the processor. In our cycle-level simulations, the Markov Prefetcher reduces the overall execution stalls due to instruction and data memory operations by an average of 54% for various commercial benchmarks while only using two thirds the memory of a demand-fetch cache organization.

Prefetching using Markov predictors

Trends in VLSI technology scaling demand that future computing devices be narrowly focused to achieve high performance and high efficiency, yet also target the high volumes and low costs of widely applicable general purpose designs. To address these conflicting requirements, we propose a modular reconfigurable architecture called Smart Memories, targeted at computing needs in the 0.1m technology generation. A Smart Memories chip is made up of many processing tiles, each containing local memory, local interconnect, and a processor core. For efficient computation under a wide class of possible applications, the memories, the wires, and the computational model can all be altered to match the applications. To show the applicability of this design, two very different machines at opposite ends of the architectural spectrum, the Imagine stream processor and the Hydra speculative multiprocessor, are mapped onto the Smart Memories computing substrate. Simulations of the mappings show that the Smart Memories architecture can successfully map these architectures with only modest performance degradation.

Smart Memories: a modular reconfigurable architecture

This paper makes the case that pin bandwidth will be a critical consideration for future microprocessors. We show that many of the techniques used to tolerate growing memory latencies do so at the expense of increased bandwidth requirements. Using a decomposition of execution time, we show that for modern processors that employ aggressive memory latency tolerance techniques, wasted cycles due to insufficient bandwidth generally exceed those due to raw memory latencies. Given the importance of maximizing memory bandwidth, we calculate effective pin bandwidth, then estimate optimal effective pin bandwidth. We measure these quantities by determining the amount by which both caches and minimal-traffic caches filter accesses to the lower levels of the memory hierarchy. We see that there is a gap that can exceed two orders of magnitude between the total memory traffic generated by caches and the minimal-traffic caches---implying that the potential exists to increase effective pin bandwidth substantially. We decompose this traffic gap into four factors, and show they contribute quite differently to traffic reduction for different benchmarks. We conclude that, in the short term, pin bandwidth limitations will make more complex on-chip caches cost-effective. For example, flexible caches may allow individual applications to choose from a range of caching policies. In the long term, we predict that off-chip accesses will be so expensive that all system memory will reside on one or more processor chips.

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Memory Bandwidth Limitations of Future Microprocessors

Today's commodity microprocessors require a low latency memory system to achieve high sustained performance. The conventional high-performance memory system provides fast data access via a large secondary cache. But large secondary caches can be expensive, particularly in large-scale parallel systems with many processors (and thus many caches).We evaluate a memory system design that can be both cost-effective as well as provide better performance, particularly for scientific workloads: a single level of (on-chip) cache backed up only by Jouppi's stream buffers [10] and a main memory. This memory system requires very little hardware compared to a large secondary cache and doesn't require modifications to commodity processors. We use trace-driven simulation of fifteen scientific applications from the NAS and PERFECT suites in our evaluation. We present two techniques to enhance the effectiveness of Jouppi's original stream buffers: filtering schemes to reduce their memory bandwidth requirement and a scheme that enables stream buffers to prefetch data being accessed in large strides. Our results show that, for the majority of our benchmarks, stream buffers can attain hit rates that are comparable to typical hit rates of secondary caches. Also, we find that as the data-set size of the scientific workload increases the performance of streams typically improves relative to secondary cache performance, showing that streams are more scalable to large data-set sizes.

R. E. Kessler

Papers

Evaluating stream buffers as a secondary cache replacement