Workload Characterization Of Emerging Computer Applications

The formal study of program behavior has become an essential ingredient in guiding the design of new computer architectures. Accurate characterization of applications leads to efficient design of high performing architectures. Quantitative and analytical characterization of workloads is important to understand and exploit the interesting features of workloads. This book includes ten chapters on various aspects of workload characterizati on. File caching characteristics of the industry-standard web-serving benchmark SPECweb99 are presented by Keller et al. in Chapter 1, while value locality of SPECJVM98 benchmarks are characterized by Rychlik et al. in Chapter 2. SPECJVM98 benchmarks are visited again in Chapter 3, where Tao et al. study the operating system activity in Java programs. In Chapter 4, KleinOsowski et al. describe how the SPEC2000 CPU benchmark suite may be adapted for computer architecture research and present the small, representative input data sets they created to reduce simulation time without compromising on accuracy. Their research has been recognized by the Standard Performance Evaluation Corporation (SPEC) and is listed on the official SPEC website, http://www. spec. org/osg/cpu2000/research/umnl. The main contribution of Chapter 5 is the proposal of a new measure called locality surface to characterize locality of reference in programs. Sorenson et al. describe how a three-dimensional surface can be used to represent both of programs. In Chapter 6, Thornock et al.

The advent of the world-wide web and web-based applications have dramatically changed the nature of computer applications. Computer system design, in the light of these changes, involves understanding these modem workloads, identifying bottlenecks during their execution, and appropriately tailoring microprocessors, memory systems, and the overall system to minimize bottlenecks. This book contains ten chapters dealing with several contemporary programming paradigms including Java, web server and database workloads. The first two chapters concentrate on Java. While Barisone et al.'s characterization in Chapter 1 deals with instruction set usage of Java applications, Kim et al.'s analysis in Chapter 2 focuses on memory referencing behavior of Java workloads. Several applications including the SPECjvm98 suite are studied using interpreter and Just-In-Time (TIT) compilers. Barisone et al.'s work includes an analytical model to compute the utilization of various functional units. Kim et al. present information on locality, live-range of objects, object lifetime distribution, etc. Studying database workloads has been a challenge to research groups, due to the difficulty in accessing standard benchmarks. Configuring hardware and software for database benchmarks such as those from the Transactions Processing Council (TPC) requires extensive effort. In Chapter 3, Keeton and Patterson present a simplified workload (microbenchmark) that approximates the characteristics of complex standardized benchmarks.

Understand the fundamental factors of data storage system performance and master an essential analytical skill using block trace via applications such as MATLAB and Python tools. You will increase your productivity and learn the best techniques for doing specific tasks (such as analyzing the IO pattern in a quantitative way, identifying the storage system bottleneck, and designing the cache policy). In the new era of IoT, big data, and cloud systems, better performance and higher density of storage systems has become crucial. To increase data storage density, new techniques have evolved and hybrid and parallel access techniques—together with specially designed IO scheduling and data migration algorithms—are being deployed to develop high-performance data storage solutions. Among the various storage system performance analysis techniques, IO event trace analysis (block-level trace analysis particularly) is one of the most common approaches for system optimization and design. However, the task of completing a systematic survey is challenging and very few works on this topic exist. Block Trace Analysis and Storage System Optimization brings together theoretical analysis (such as IO qualitative properties and quantitative metrics) and practical tools (such as trace parsing, analysis, and results reporting perspectives). The book provides content on block-level trace analysis techniques, and includes case studies to illustrate how these techniques and tools can be applied in real applications (such as SSHD, RAID, Hadoop, and Ceph systems). What You’ll Learn Understand the fundamental factors of data storage system performance Master an essential analytical skill using block trace via various applications Distinguish how the IO pattern differs in the block level from the file level Know how the sequential HDFS request becomes “fragmented” in final storage devices Perform trace analysis tasks with a tool based on the MATLAB and Python platforms Who This Book Is For IT professionals interested in storage system performance optimization: network administrators, data storage managers, data storage engineers, storage network engineers, systems engineers

The LNCS series reports state-of-the-art results in computer science research, development, and education, at a high level and in both printed and electronic form. Enjoying tight cooperation with the R&D community, with numerous individuals, as well as with prestigious organizations and societies, LNCS has grown into the most comprehensive computer science research forum available. The scope of LNCS, including its subseries LNAI and LNBI, spans the whole range of computer science and information technology including interdisciplinary topics in a variety of application fields. In parallel to the printed book, each new volume is published electronically in LNCS Online.

This book constitutes the thoroughly revised selected papers of the 4th and 5th workshops on Big Data Benchmarks, Performance Optimization, and Emerging Hardware, BPOE 4 and BPOE 5, held respectively in Salt Lake City, in March 2014, and in Hangzhou, in September 2014. The 16 papers presented were carefully reviewed and selected from 30 submissions. Both workshops focus on architecture and system support for big data systems, such as benchmarking; workload characterization; performance optimization and evaluation; emerging hardware.

Euro-Par – the European Conference on Parallel Computing – is an international conference series dedicated to the promotion and advancement of all aspects of parallel computing. The major themes can be divided into the broad categories of hardware, software, algorithms, and applications for parallel computing. The objective of Euro-Par is to provide a forum within which to promote the dev- opment of parallel computing both as an industrial technique and an academic discipline, extending the frontiers of both the state of the art and the state of the practice. This is particularlyimportant at a time when parallel computing is undergoing strong and sustained development and experiencing real industrial take-up. The main audience for and participants in Euro-Par are researchers in academic departments, government laboratories, and industrial organizations. Euro-Par aims to become the primarychoice of such professionals for the p- sentation of new results in their speci?c areas. Euro-Par is also interested in applications that demonstrate the e?ectiveness of the main Euro-Par themes. Euro-Par has its own Internet domain with a permanent website where the historyof the conference series is described: http://www. euro-par. org. The Euro-Par conference series is sponsored bythe Association of Computer - chineryand the International Federation of Information Processing. Euro-Par 2002 at Paderborn, Germany Euro-Par 2002 was organized bythe Paderborn Center for Parallel Comput- 2 2 ing (PC ) and was held at the Heinz Nixdorf MuseumsForum (HNF).

With the end of Moore’s Law, domain-specific architecture (DSA) has become a crucial mode of implementing future computing architectures. This book discusses the system-level design methodology of DSAs and their applications, providing a unified design process that guarantees functionality, performance, energy efficiency, and real-time responsiveness for the target application. DSAs often start from domain-specific algorithms or applications, analyzing the characteristics of algorithmic applications, such as computation, memory access, and communication, and proposing the heterogeneous accelerator architecture suitable for that particular application. This book places particular focus on accelerator hardware platforms and distributed systems for various novel applications, such as machine learning, data mining, neural networks, and graph algorithms, and also covers RISC-V open-source instruction sets. It briefly describes the system design methodology based on DSAs and presents the latest research results in academia around domain-specific acceleration architectures. Providing cutting-edge discussion of big data and artificial intelligence scenarios in contemporary industry and typical DSA applications, this book appeals to industry professionals as well as academicians researching the future of computing in these areas.