Chip Multiprocessor Architecture: Techniques to Improve Throughput and Latency
Morgan & Claypool Publishers, 2007 - Architecture - 145 pages
Chip multiprocessors - also called multi-core microprocessors or CMPs for short - are now the only way to build high-performance microprocessors, for a variety of reasons. Large uniprocessors are no longer scaling in performance, because it is only possible to extract a limited amount of parallelism from a typical instruction stream using conventional superscalar instruction issue techniques. In addition, one cannot simply ratchet up the clock speed on today's processors, or the power dissipation will become prohibitive in all but water-cooled systems. Compounding these problems is the simple fact that with the immense numbers of transistors available on today's microprocessor chips, it is too costly to design and debug ever-larger processors every year or two.CMPs avoid these problems by filling up a processor die with multiple, relatively simpler processor cores instead of just one huge core. The exact size of a CMP‚€™s cores can vary from very simple pipelines to moderately complex superscalar processors, but once a core has been selected the CMP‚€™s performance can easily scale across silicon process generations simply by stamping down more copies of the hard-to-design, high-speed processor core in each successive chip generation. In addition, parallel code execution, obtained by spreading multiple threads of execution across the various cores, can achieve significantly higher performance than would be possible using only a single core. While parallel threads are already common in many useful workloads, there are still important workloads that are hard to divide into parallel threads. The low inter-processor communication latency between the cores in a CMP helps make a much wider range of applications viable candidates for parallel execution than was possible with conventional, multi-chip multiprocessors; nevertheless, limited parallelism in key applications is the main factor limiting acceptance of CMPs in some types of systems.After a discussion of the basic pros and cons of CMPs when they are compared with conventional uniprocessors, this book examines how CMPs can best be designed to handle two radically different kinds of workloads that are likely to be used with a CMP: highly parallel, throughput-sensitive applications at one end of the spectrum, and less parallel, latency-sensitive applications at the other. Throughput-sensitive applications, such as server workloads that handle many independent transactions at once, require careful balancing of all parts of a CMP that can limit throughput, such as the individual cores, on-chip cache memory, and off-chip memory interfaces. Several studies and example systems, such as the Sun Niagara, that examine the necessary tradeoffs are presented here. In contrast, latency-sensitive applications ‚€” many desktop applications fall into this category ‚€” require a focus on reducing inter-core communication latency and applying techniques to help programmers divide their programs into multiple threads as easily as possible. This book discusses many techniques that can be used in CMPs to simplify parallel programming, with an emphasis on research directions proposed at Stanford University. To illustrate the advantages possible with a CMP using a couple of solid examples, extra focus is given to thread-level speculation (TLS), a way to automatically break up nominally sequential applications into parallel threads on a CMP, and transactional memory. This model can greatly simplify manual parallel programming by using hardware ‚€” instead of conventional software locks ‚€” to enforce atomic code execution of blocks of instructions, a technique that makes parallel coding much less error-prone.Contents: The Case for CMPs / Improving Throughput / Improving Latency Automatically / Improving Latency using Manual Parallel Programming / A Multicore World: The Future of CMPs
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For non-practitioner(s) of computer system design or processor design, this concise book definitely helps. Anyone with enough EE background (curiosity can make up for this too!) wanting to get up to speed with recent trends in processor design, will find this useful. This is important because sooner or later, you will deal with computer systems using these innovations.
Workload charectarization has a huge influence in computer system design, as well as processor design.
This book focuses on the Workload charecterization issues (Throughput sensitive and Latency sensitive workloads), and how they affect processor design. You might have to look up the various benchmarks referred to in this book.
Chapter 1 makes the case of CMT (chip multi-threading architecture). Chapter 2 covers Application workloads may already come with a good degree of threading already exists, and how CMT architecture exploits this property. Chapter 3 covers other considerations where legacy code with little or no direct threading can still exploit the CMT benefits via automatic Thread Level Parallelism from sequential code. It also covers recent techniques where you can get completely automated parallelization of java code. Chapter 4 covers manual programming techniques for exploiting CMT.
32 AUTOMATED PARALLELIZATION USING THREADLEVEL SPECULATION TLS
332 Adding TLS to Hydra
333 Using Feedback from Violation Statistics
334 Performance Analysis
The JRPM System
34 CONCLUDING THOUGHTS ON AUTOMATED PARALLELIZATION
Improving Latency Using Manual Parallel Programming
212 Maximizing the Number of Cores on the Die
213 Providing Sufficient Cache and Memory Bandwidth
The Niagara Server CMP
The Niagara 2 Server CMP
224 Simple Core Limitations
23 GENERAL SERVER CMP ANALYSIS
232 Choosing Design Datapoints
Improving Latency Automatically
41 USING TLS SUPPORT AS TRANSACTIONAL MEMORY
Parallelizing Heapsort Using TLS
412 Parallelizing SPEC2000 with TLS
MORE GENERALIZED TRANSACTIONAL MEMORY
421 TCC HARDWARE
422 TCC Software
423 TCC Performance
43 MIXING TRANSACTIONAL MEMORY AND CONVENTIONAL SHARED MEMORY
A Multicore World The Future of CMPs