An Evaluation of the TRIPS Computer System
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EEMBC and the Purposes of Embedded Processor Benchmarking Markus Levy, President
EEMBC and the Purposes of Embedded Processor Benchmarking Markus Levy, President ISPASS 2005 Certified Performance Analysis for Embedded Systems Designers EEMBC: A Historical Perspective • Began as an EDN Magazine project in April 1997 • Replace Dhrystone • Have meaningful measure for explaining processor behavior • Developed business model • Invited worldwide processor vendors • A consortium was born 1 EEMBC Membership • Board Member • Membership Dues: $30,000 (1st year); $16,000 (subsequent years) • Access and Participation on ALL Subcommittees • Full Voting Rights • Subcommittee Member • Membership Dues Are Subcommittee Specific • Access to Specific Benchmarks • Technical Involvement Within Subcommittee • Help Determine Next Generation Benchmarks • Special Academic Membership EEMBC Philosophy: Standardized Benchmarks and Certified Scores • Member derived benchmarks • Determine the standard, the process, and the benchmarks • Open to industry feedback • Ensures all processor/compiler vendors are running the same tests • Certification process ensures credibility • All benchmark scores officially validated before publication • The entire benchmark environment must be disclosed 2 Embedded Industry: Represented by Very Diverse Applications • Networking • Storage, low- and high-end routers, switches • Consumer • Games, set top boxes, car navigation, smartcards • Wireless • Cellular, routers • Office Automation • Printers, copiers, imaging • Automotive • Engine control, Telematics Traditional Division of Embedded Applications Low High Power -
Cloud Workbench a Web-Based Framework for Benchmarking Cloud Services
Bachelor August 12, 2014 Cloud WorkBench A Web-Based Framework for Benchmarking Cloud Services Joel Scheuner of Muensterlingen, Switzerland (10-741-494) supervised by Prof. Dr. Harald C. Gall Philipp Leitner, Jürgen Cito software evolution & architecture lab Bachelor Cloud WorkBench A Web-Based Framework for Benchmarking Cloud Services Joel Scheuner software evolution & architecture lab Bachelor Author: Joel Scheuner, [email protected] Project period: 04.03.2014 - 14.08.2014 Software Evolution & Architecture Lab Department of Informatics, University of Zurich Acknowledgements This bachelor thesis constitutes the last milestone on the way to my first academic graduation. I would like to thank all the people who accompanied and supported me in the last four years of study and work in industry paving the way to this thesis. My personal thanks go to my parents supporting me in many ways. The decision to choose a complex topic in an area where I had no personal experience in advance, neither theoretically nor technologically, made the past four and a half months challenging, demanding, work-intensive, but also very educational which is what remains in good memory afterwards. Regarding this thesis, I would like to offer special thanks to Philipp Leitner who assisted me during the whole process and gave valuable advices. Moreover, I want to thank Jürgen Cito for his mainly technologically-related recommendations, Rita Erne for her time spent with me on language review sessions, and Prof. Harald Gall for giving me the opportunity to write this thesis at the Software Evolution & Architecture Lab at the University of Zurich and providing fundings and infrastructure to realize this thesis. -
Performance and Energy Efficient Network-On-Chip Architectures
Linköping Studies in Science and Technology Dissertation No. 1130 Performance and Energy Efficient Network-on-Chip Architectures Sriram R. Vangal Electronic Devices Department of Electrical Engineering Linköping University, SE-581 83 Linköping, Sweden Linköping 2007 ISBN 978-91-85895-91-5 ISSN 0345-7524 ii Performance and Energy Efficient Network-on-Chip Architectures Sriram R. Vangal ISBN 978-91-85895-91-5 Copyright Sriram. R. Vangal, 2007 Linköping Studies in Science and Technology Dissertation No. 1130 ISSN 0345-7524 Electronic Devices Department of Electrical Engineering Linköping University, SE-581 83 Linköping, Sweden Linköping 2007 Author email: [email protected] Cover Image A chip microphotograph of the industry’s first programmable 80-tile teraFLOPS processor, which is implemented in a 65-nm eight-metal CMOS technology. Printed by LiU-Tryck, Linköping University Linköping, Sweden, 2007 Abstract The scaling of MOS transistors into the nanometer regime opens the possibility for creating large Network-on-Chip (NoC) architectures containing hundreds of integrated processing elements with on-chip communication. NoC architectures, with structured on-chip networks are emerging as a scalable and modular solution to global communications within large systems-on-chip. NoCs mitigate the emerging wire-delay problem and addresses the need for substantial interconnect bandwidth by replacing today’s shared buses with packet-switched router networks. With on-chip communication consuming a significant portion of the chip power and area budgets, there is a compelling need for compact, low power routers. While applications dictate the choice of the compute core, the advent of multimedia applications, such as three-dimensional (3D) graphics and signal processing, places stronger demands for self-contained, low-latency floating-point processors with increased throughput. -
Automatic Benchmark Profiling Through Advanced Trace Analysis Alexis Martin, Vania Marangozova-Martin
Automatic Benchmark Profiling through Advanced Trace Analysis Alexis Martin, Vania Marangozova-Martin To cite this version: Alexis Martin, Vania Marangozova-Martin. Automatic Benchmark Profiling through Advanced Trace Analysis. [Research Report] RR-8889, Inria - Research Centre Grenoble – Rhône-Alpes; Université Grenoble Alpes; CNRS. 2016. hal-01292618 HAL Id: hal-01292618 https://hal.inria.fr/hal-01292618 Submitted on 24 Mar 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Automatic Benchmark Profiling through Advanced Trace Analysis Alexis Martin , Vania Marangozova-Martin RESEARCH REPORT N° 8889 March 23, 2016 Project-Team Polaris ISSN 0249-6399 ISRN INRIA/RR--8889--FR+ENG Automatic Benchmark Profiling through Advanced Trace Analysis Alexis Martin ∗ † ‡, Vania Marangozova-Martin ∗ † ‡ Project-Team Polaris Research Report n° 8889 — March 23, 2016 — 15 pages Abstract: Benchmarking has proven to be crucial for the investigation of the behavior and performances of a system. However, the choice of relevant benchmarks still remains a challenge. To help the process of comparing and choosing among benchmarks, we propose a solution for automatic benchmark profiling. It computes unified benchmark profiles reflecting benchmarks’ duration, function repartition, stability, CPU efficiency, parallelization and memory usage. -
Computer Architecture: Dataflow (Part I)
Computer Architecture: Dataflow (Part I) Prof. Onur Mutlu Carnegie Mellon University A Note on This Lecture n These slides are from 18-742 Fall 2012, Parallel Computer Architecture, Lecture 22: Dataflow I n Video: n http://www.youtube.com/watch? v=D2uue7izU2c&list=PL5PHm2jkkXmh4cDkC3s1VBB7- njlgiG5d&index=19 2 Some Required Dataflow Readings n Dataflow at the ISA level q Dennis and Misunas, “A Preliminary Architecture for a Basic Data Flow Processor,” ISCA 1974. q Arvind and Nikhil, “Executing a Program on the MIT Tagged- Token Dataflow Architecture,” IEEE TC 1990. n Restricted Dataflow q Patt et al., “HPS, a new microarchitecture: rationale and introduction,” MICRO 1985. q Patt et al., “Critical issues regarding HPS, a high performance microarchitecture,” MICRO 1985. 3 Other Related Recommended Readings n Dataflow n Gurd et al., “The Manchester prototype dataflow computer,” CACM 1985. n Lee and Hurson, “Dataflow Architectures and Multithreading,” IEEE Computer 1994. n Restricted Dataflow q Sankaralingam et al., “Exploiting ILP, TLP and DLP with the Polymorphous TRIPS Architecture,” ISCA 2003. q Burger et al., “Scaling to the End of Silicon with EDGE Architectures,” IEEE Computer 2004. 4 Today n Start Dataflow 5 Data Flow Readings: Data Flow (I) n Dennis and Misunas, “A Preliminary Architecture for a Basic Data Flow Processor,” ISCA 1974. n Treleaven et al., “Data-Driven and Demand-Driven Computer Architecture,” ACM Computing Surveys 1982. n Veen, “Dataflow Machine Architecture,” ACM Computing Surveys 1986. n Gurd et al., “The Manchester prototype dataflow computer,” CACM 1985. n Arvind and Nikhil, “Executing a Program on the MIT Tagged-Token Dataflow Architecture,” IEEE TC 1990. -
Automatic Benchmark Profiling Through Advanced Trace Analysis
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Hal - Université Grenoble Alpes Automatic Benchmark Profiling through Advanced Trace Analysis Alexis Martin, Vania Marangozova-Martin To cite this version: Alexis Martin, Vania Marangozova-Martin. Automatic Benchmark Profiling through Advanced Trace Analysis. [Research Report] RR-8889, Inria - Research Centre Grenoble { Rh^one-Alpes; Universit´eGrenoble Alpes; CNRS. 2016. <hal-01292618> HAL Id: hal-01292618 https://hal.inria.fr/hal-01292618 Submitted on 24 Mar 2016 HAL is a multi-disciplinary open access L'archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destin´eeau d´ep^otet `ala diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publi´esou non, lished or not. The documents may come from ´emanant des ´etablissements d'enseignement et de teaching and research institutions in France or recherche fran¸caisou ´etrangers,des laboratoires abroad, or from public or private research centers. publics ou priv´es. Automatic Benchmark Profiling through Advanced Trace Analysis Alexis Martin , Vania Marangozova-Martin RESEARCH REPORT N° 8889 March 23, 2016 Project-Team Polaris ISSN 0249-6399 ISRN INRIA/RR--8889--FR+ENG Automatic Benchmark Profiling through Advanced Trace Analysis Alexis Martin ∗ † ‡, Vania Marangozova-Martin ∗ † ‡ Project-Team Polaris Research Report n° 8889 — March 23, 2016 — 15 pages Abstract: Benchmarking has proven to be crucial for the investigation of the behavior and performances of a system. However, the choice of relevant benchmarks still remains a challenge. To help the process of comparing and choosing among benchmarks, we propose a solution for automatic benchmark profiling. -
Introduction to Digital Signal Processors
INTRODUCTION TO Accumulator architecture DIGITAL SIGNAL PROCESSORS Memory-register architecture Prof. Brian L. Evans in collaboration with Niranjan Damera-Venkata and Magesh Valliappan Load-store architecture Embedded Signal Processing Laboratory The University of Texas at Austin Austin, TX 78712-1084 http://anchovy.ece.utexas.edu/ Outline n Signal processing applications n Conventional DSP architecture n Pipelining in DSP processors n RISC vs. DSP processor architectures n TI TMS320C6x VLIW DSP architecture n Signal and image processing applications n Signal processing on general-purpose processors n Conclusion 2 Signal Processing Applications n Low-cost embedded systems 4 Modems, cellular telephones, disk drives, printers n High-throughput applications 4 Halftoning, radar, high-resolution sonar, tomography n PC based multimedia 4 Compression/decompression of audio, graphics, video n Embedded processor requirements 4 Inexpensive with small area and volume 4 Deterministic interrupt service routine latency 4 Low power: ~50 mW (TMS320C5402/20: 0.32 mA/MIP) 3 Conventional DSP Architecture n High data throughput 4 Harvard architecture n Separate data memory/bus and program memory/bus n Three reads and one or two writes per instruction cycle 4 Short deterministic interrupt service routine latency 4 Multiply-accumulate (MAC) in a single instruction cycle 4 Special addressing modes supported in hardware n Modulo addressing for circular buffers (e.g. FIR filters) n Bit-reversed addressing (e.g. fast Fourier transforms) 4Instructions to keep the -
Configurable Fine-Grain Protection for Multicore Processor Virtualization 1
Configurable Fine-Grain Protection for Multicore Processor Virtualization David Wentzlaff1, Christopher J. Jackson2, Patrick Griffin3, and Anant Agarwal2 [email protected], [email protected], griffi[email protected], [email protected] 1Princeton University 2Tilera Corp. 3Google Inc. Abstract TLB Access DMA Engine “User” Network I/O Network 2 2 2 2 1 3 1 3 1 3 1 3 Multicore architectures, with their abundant on-chip re- 0 0 0 0 sources, are effectively collections of systems-on-a-chip. The protection system for these architectures must support Key: 0 – User Code, 1 – OS, 2 – Hypervisor, 3 – Hypervisor Debugger multiple concurrently executing operating systems (OSes) Figure 1. With CFP, system software can dy• with different needs, and manage and protect the hard- namically set the privilege level needed to ac• ware’s novel communication mechanisms and hardware cess each fine•grain processor resource. features. Traditional protection systems are insufficient; they protect supervisor from user code, but typically do not protect one system from another, and only support fixed as- ticore systems, a protection system must both temporally signment of resources to protection levels. In this paper, protect and spatially isolate access to resources. Spatial iso- we propose an alternative to traditional protection systems lation is the need to isolate different system software stacks which we call configurable fine-grain protection (CFP). concurrently executing on spatially disparate cores in a mul- CFP enables the dynamic assignment of in-core resources ticore system. Spatial isolation is especially important now to protection levels. We investigate how CFP enables differ- that multicore systems have directly accessible networks ent system software stacks to utilize the same configurable connecting cores to other cores and cores to I/O devices. -
BOOM): an Industry- Competitive, Synthesizable, Parameterized RISC-V Processor
The Berkeley Out-of-Order Machine (BOOM): An Industry- Competitive, Synthesizable, Parameterized RISC-V Processor Christopher Celio David A. Patterson Krste Asanović Electrical Engineering and Computer Sciences University of California at Berkeley Technical Report No. UCB/EECS-2015-167 http://www.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-167.html June 13, 2015 Copyright © 2015, by the author(s). All rights reserved. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission. The Berkeley Out-of-Order Machine (BOOM): An Industry-Competitive, Synthesizable, Parameterized RISC-V Processor Christopher Celio, David Patterson, and Krste Asanovic´ University of California, Berkeley, California 94720–1770 [email protected] BOOM is a work-in-progress. Results shown are prelimi- nary and subject to change as of 2015 June. I$ L1 D$ (32k) L2 data 1. The Berkeley Out-of-Order Machine BOOM is a synthesizable, parameterized, superscalar out- exe of-order RISC-V core designed to serve as the prototypical baseline processor for future micro-architectural studies of uncore regfile out-of-order processors. Our goal is to provide a readable, issue open-source implementation for use in education, research, exe and industry. uncore BOOM is written in roughly 9,000 lines of the hardware L2 data (256k) construction language Chisel. -
A Free, Commercially Representative Embedded Benchmark Suite
MiBench: A free, commercially representative embedded benchmark suite Matthew R. Guthaus, Jeffrey S. Ringenberg, Dan Ernst, Todd M. Austin, Trevor Mudge, Richard B. Brown {mguthaus,jringenb,ernstd,taustin,tnm,brown}@eecs.umich.edu The University of Michigan Electrical Engineering and Computer Science 1301 Beal Ave., Ann Arbor, MI 48109-2122 Abstract Among the common features of these This paper examines a set of commercially microarchitectures are deep pipelines, significant representative embedded programs and compares them instruction level parallelism, sophisticated branch to an existing benchmark suite, SPEC2000. A new prediction schemes, and large caches. version of SimpleScalar that has been adapted to the Although this class of machines has been the chief ARM instruction set is used to characterize the focus of the computer architecture community, performance of the benchmarks using configurations relatively few microprocessors are employed in this similar to current and next generation embedded market segment. The vast majority of microprocessors processors. Several characteristics distinguish the are employed in embedded applications [8]. Although representative embedded programs from the existing many are just inexpensive microcontrollers, their SPEC benchmarks including instruction distribution, combined sales account for nearly half of all memory behavior, and available parallelism. The microprocessor revenue. Furthermore, the embedded embedded benchmarks, called MiBench, are freely application domain is the fastest growing market available to all researchers. segment in the microprocessor industry. The wide range of applications makes it difficult to 1. Introduction characterize the embedded domain. In fact, an Performance based design has made benchmarking embedded benchmark suite should reflect this by a critical part of the design process [1]. -
Low-Power High Performance Computing
Low-Power High Performance Computing Panagiotis Kritikakos August 16, 2011 MSc in High Performance Computing The University of Edinburgh Year of Presentation: 2011 Abstract The emerging development of computer systems to be used for HPC require a change in the architecture for processors. New design approaches and technologies need to be embraced by the HPC community for making a case for new approaches in system design for making it possible to be used for Exascale Supercomputers within the next two decades, as well as to reduce the CO2 emissions of supercomputers and scientific clusters, leading to greener computing. Power is listed as one of the most important issues and constraint for future Exascale systems. In this project we build a hybrid cluster, investigating, measuring and evaluating the performance of low-power CPUs, such as Intel Atom and ARM (Marvell 88F6281) against commodity Intel Xeon CPU that can be found within standard HPC and data-center clusters. Three main factors are considered: computational performance and efficiency, power efficiency and porting effort. Contents 1 Introduction 1 1.1 Report organisation . 2 2 Background 3 2.1 RISC versus CISC . 3 2.2 HPC Architectures . 4 2.2.1 System architectures . 4 2.2.2 Memory architectures . 5 2.3 Power issues in modern HPC systems . 9 2.4 Energy and application efficiency . 10 3 Literature review 12 3.1 Green500 . 12 3.2 Supercomputing in Small Spaces (SSS) . 12 3.3 The AppleTV Cluster . 13 3.4 Sony Playstation 3 Cluster . 13 3.5 Microsoft XBox Cluster . 14 3.6 IBM BlueGene/Q . -
CG-Ooo Energy-Efficient Coarse-Grain Out-Of-Order Execution
CG-OoO Energy-Efficient Coarse-Grain Out-of-Order Execution Milad Mohammadi⋆, Tor M. Aamodt†, William J. Dally⋆‡ ⋆Stanford University, †University of British Columbia, ‡NVIDIA Research [email protected], [email protected], [email protected] ABSTRACT CG-OoO model to make it even more energy efficient. We introduce the Coarse-Grain Out-of-Order (CG- Despite the significant achievements in improving en- OoO) general purpose processor designed to achieve ergy and performance properties of the OoO proces- close to In-Order processor energy while maintaining sor in the recent years [2], studies show the energy Out-of-Order (OoO) performance. CG-OoO is an and performance attributes of the OoO execution model energy-performance proportional general purpose remain superlinearly proportional [3, 4]. Studies indi- architecture that scales according to the program cate control speculation and dynamic scheduling tech- load1. Block-level code processing is at the heart of nique amount to 88% and 10% of the OoO superior the this architecture; CG-OoO speculates, fetches, performance compared to the In-Order (InO) proces- schedules, and commits code at block-level granu- sor [5]. Scheduling and speculation in OoO is performed larity. It eliminates unnecessary accesses to energy at instruction granularity regardless of the instruction consuming tables, and turns large tables into smaller type even though they are mainly effective during un- and distributed tables that are cheaper to access. predictable dynamic events (e.g. unpredictable cache CG-OoO leverages compiler-level code optimizations misses) [5]. Furthermore, our studies show speculation to deliver efficient static code, and exploits dynamic and dynamic scheduling amount to 67% and 51% of instruction-level parallelism and block-level parallelism.