An Evaluation of the TRIPS Computer System
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Memory Centric Characterization and Analysis of SPEC CPU2017 Suite
Session 11: Performance Analysis and Simulation ICPE ’19, April 7–11, 2019, Mumbai, India Memory Centric Characterization and Analysis of SPEC CPU2017 Suite Sarabjeet Singh Manu Awasthi [email protected] [email protected] Ashoka University Ashoka University ABSTRACT These benchmarks have become the standard for any researcher or In this paper, we provide a comprehensive, memory-centric charac- commercial entity wishing to benchmark their architecture or for terization of the SPEC CPU2017 benchmark suite, using a number of exploring new designs. mechanisms including dynamic binary instrumentation, measure- The latest offering of SPEC CPU suite, SPEC CPU2017, was re- ments on native hardware using hardware performance counters leased in June 2017 [8]. SPEC CPU2017 retains a number of bench- and operating system based tools. marks from previous iterations but has also added many new ones We present a number of results including working set sizes, mem- to reflect the changing nature of applications. Some recent stud- ory capacity consumption and memory bandwidth utilization of ies [21, 24] have already started characterizing the behavior of various workloads. Our experiments reveal that, on the x86_64 ISA, SPEC CPU2017 applications, looking for potential optimizations to SPEC CPU2017 workloads execute a significant number of mem- system architectures. ory related instructions, with approximately 50% of all dynamic In recent years the memory hierarchy, from the caches, all the instructions requiring memory accesses. We also show that there is way to main memory, has become a first class citizen of computer a large variation in the memory footprint and bandwidth utilization system design. -
Overview of the SPEC Benchmarks
9 Overview of the SPEC Benchmarks Kaivalya M. Dixit IBM Corporation “The reputation of current benchmarketing claims regarding system performance is on par with the promises made by politicians during elections.” Standard Performance Evaluation Corporation (SPEC) was founded in October, 1988, by Apollo, Hewlett-Packard,MIPS Computer Systems and SUN Microsystems in cooperation with E. E. Times. SPEC is a nonprofit consortium of 22 major computer vendors whose common goals are “to provide the industry with a realistic yardstick to measure the performance of advanced computer systems” and to educate consumers about the performance of vendors’ products. SPEC creates, maintains, distributes, and endorses a standardized set of application-oriented programs to be used as benchmarks. 489 490 CHAPTER 9 Overview of the SPEC Benchmarks 9.1 Historical Perspective Traditional benchmarks have failed to characterize the system performance of modern computer systems. Some of those benchmarks measure component-level performance, and some of the measurements are routinely published as system performance. Historically, vendors have characterized the performances of their systems in a variety of confusing metrics. In part, the confusion is due to a lack of credible performance information, agreement, and leadership among competing vendors. Many vendors characterize system performance in millions of instructions per second (MIPS) and millions of floating-point operations per second (MFLOPS). All instructions, however, are not equal. Since CISC machine instructions usually accomplish a lot more than those of RISC machines, comparing the instructions of a CISC machine and a RISC machine is similar to comparing Latin and Greek. 9.1.1 Simple CPU Benchmarks Truth in benchmarking is an oxymoron because vendors use benchmarks for marketing purposes. -
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. -
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. -
3 — Arithmetic for Computers 2 MIPS Arithmetic Logic Unit (ALU) Zero Ovf
Chapter 3 Arithmetic for Computers 1 § 3.1Introduction Arithmetic for Computers Operations on integers Addition and subtraction Multiplication and division Dealing with overflow Floating-point real numbers Representation and operations Rechnerstrukturen 182.092 3 — Arithmetic for Computers 2 MIPS Arithmetic Logic Unit (ALU) zero ovf 1 Must support the Arithmetic/Logic 1 operations of the ISA A 32 add, addi, addiu, addu ALU result sub, subu 32 mult, multu, div, divu B 32 sqrt 4 and, andi, nor, or, ori, xor, xori m (operation) beq, bne, slt, slti, sltiu, sltu With special handling for sign extend – addi, addiu, slti, sltiu zero extend – andi, ori, xori overflow detection – add, addi, sub Rechnerstrukturen 182.092 3 — Arithmetic for Computers 3 (Simplyfied) 1-bit MIPS ALU AND, OR, ADD, SLT Rechnerstrukturen 182.092 3 — Arithmetic for Computers 4 Final 32-bit ALU Rechnerstrukturen 182.092 3 — Arithmetic for Computers 5 Performance issues Critical path of n-bit ripple-carry adder is n*CP CarryIn0 A0 1-bit result0 ALU B0 CarryOut0 CarryIn1 A1 1-bit result1 B ALU 1 CarryOut 1 CarryIn2 A2 1-bit result2 ALU B2 CarryOut CarryIn 2 3 A3 1-bit result3 ALU B3 CarryOut3 Design trick – throw hardware at it (Carry Lookahead) Rechnerstrukturen 182.092 3 — Arithmetic for Computers 6 Carry Lookahead Logic (4 bit adder) LS 283 Rechnerstrukturen 182.092 3 — Arithmetic for Computers 7 § 3.2 Addition and Subtraction 3.2 Integer Addition Example: 7 + 6 Overflow if result out of range Adding +ve and –ve operands, no overflow Adding two +ve operands -
Asustek Computer Inc.: Asus P6T
SPEC CFP2006 Result spec Copyright 2006-2014 Standard Performance Evaluation Corporation ASUSTeK Computer Inc. (Test Sponsor: Intel Corporation) SPECfp 2006 = 32.4 Asus P6T Deluxe (Intel Core i7-950) SPECfp_base2006 = 30.6 CPU2006 license: 13 Test date: Oct-2008 Test sponsor: Intel Corporation Hardware Availability: Jun-2009 Tested by: Intel Corporation Software Availability: Nov-2008 0 3.00 6.00 9.00 12.0 15.0 18.0 21.0 24.0 27.0 30.0 33.0 36.0 39.0 42.0 45.0 48.0 51.0 54.0 57.0 60.0 63.0 66.0 71.0 70.7 410.bwaves 70.8 21.5 416.gamess 16.8 34.6 433.milc 34.8 434.zeusmp 33.8 20.9 435.gromacs 20.6 60.8 436.cactusADM 61.0 437.leslie3d 31.3 17.6 444.namd 17.4 26.4 447.dealII 23.3 28.5 450.soplex 27.9 34.0 453.povray 26.6 24.4 454.calculix 20.5 38.7 459.GemsFDTD 37.8 24.9 465.tonto 22.3 470.lbm 50.2 30.9 481.wrf 30.9 42.1 482.sphinx3 41.3 SPECfp_base2006 = 30.6 SPECfp2006 = 32.4 Hardware Software CPU Name: Intel Core i7-950 Operating System: Windows Vista Ultimate w/ SP1 (64-bit) CPU Characteristics: Intel Turbo Boost Technology up to 3.33 GHz Compiler: Intel C++ Compiler Professional 11.0 for IA32 CPU MHz: 3066 Build 20080930 Package ID: w_cproc_p_11.0.054 Intel Visual Fortran Compiler Professional 11.0 FPU: Integrated for IA32 CPU(s) enabled: 4 cores, 1 chip, 4 cores/chip, 2 threads/core Build 20080930 Package ID: w_cprof_p_11.0.054 CPU(s) orderable: 1 chip Microsoft Visual Studio 2008 (for libraries) Primary Cache: 32 KB I + 32 KB D on chip per core Auto Parallel: Yes Secondary Cache: 256 KB I+D on chip per core File System: NTFS Continued on next page Continued on next page Standard Performance Evaluation Corporation [email protected] Page 1 http://www.spec.org/ SPEC CFP2006 Result spec Copyright 2006-2014 Standard Performance Evaluation Corporation ASUSTeK Computer Inc. -
Modeling and Analyzing CPU Power and Performance: Metrics, Methods, and Abstractions
Modeling and Analyzing CPU Power and Performance: Metrics, Methods, and Abstractions Margaret Martonosi David Brooks Pradip Bose VET NOV TES TAM EN TVM DE I VI GE T SV B NV M I NE Moore’s Law & Power Dissipation... Moore’s Law: ❚ The Good News: 2X Transistor counts every 18 months ❚ The Bad News: To get the performance improvements we’re accustomed to, CPU Power consumption will increase exponentially too... (Graphs courtesy of Fred Pollack, Intel) Why worry about power dissipation? Battery life Thermal issues: affect cooling, packaging, reliability, timing Environment Hitting the wall… ❚ Battery technology ❚ ❙ Linear improvements, nowhere Past: near the exponential power ❙ Power important for increases we’ve seen laptops, cell phones ❚ Cooling techniques ❚ Present: ❙ Air-cooled is reaching limits ❙ Power a Critical, Universal ❙ Fans often undesirable (noise, design constraint even for weight, expense) very high-end chips ❙ $1 per chip per Watt when ❚ operating in the >40W realm Circuits and process scaling can no longer solve all power ❙ Water-cooled ?!? problems. ❚ Environment ❙ SYSTEMS must also be ❙ US EPA: 10% of current electricity usage in US is directly due to power-aware desktop computers ❙ Architecture, OS, compilers ❙ Increasing fast. And doesn’t count embedded systems, Printers, UPS backup? Power: The Basics ❚ Dynamic power vs. Static power vs. short-circuit power ❙ “switching” power ❙ “leakage” power ❙ Dynamic power dominates, but static power increasing in importance ❙ Trends in each ❚ Static power: steady, per-cycle energy cost ❚ Dynamic power: power dissipation due to capacitance charging at transitions from 0->1 and 1->0 ❚ Short-circuit power: power due to brief short-circuit current during transitions. -
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. -
Continuous Profiling: Where Have All the Cycles Gone?
Continuous Profiling: Where Have All the Cycles Gone? JENNIFER M. ANDERSON, LANCE M. BERC, JEFFREY DEAN, SANJAY GHEMAWAT, MONIKA R. HENZINGER, SHUN-TAK A. LEUNG, RICHARD L. SITES, MARK T. VANDEVOORDE, CARL A. WALDSPURGER, and WILLIAM E. WEIHL Digital Equipment Corporation This article describes the Digital Continuous Profiling Infrastructure, a sampling-based profiling system designed to run continuously on production systems. The system supports multiprocessors, works on unmodified executables, and collects profiles for entire systems, including user programs, shared libraries, and the operating system kernel. Samples are collected at a high rate (over 5200 samples/sec. per 333MHz processor), yet with low overhead (1–3% slowdown for most workloads). Analysis tools supplied with the profiling system use the sample data to produce a precise and accurate accounting, down to the level of pipeline stalls incurred by individual instructions, of where time is being spent. When instructions incur stalls, the tools identify possible reasons, such as cache misses, branch mispredictions, and functional unit contention. The fine-grained instruction-level analysis guides users and automated optimizers to the causes of performance problems and provides important insights for fixing them. Categories and Subject Descriptors: C.4 [Computer Systems Organization]: Performance of Systems; D.2.2 [Software Engineering]: Tools and Techniques—profiling tools; D.2.6 [Pro- gramming Languages]: Programming Environments—performance monitoring; D.4 [Oper- ating Systems]: General; D.4.7 [Operating Systems]: Organization and Design; D.4.8 [Operating Systems]: Performance General Terms: Performance Additional Key Words and Phrases: Profiling, performance understanding, program analysis, performance-monitoring hardware An earlier version of this article appeared at the 16th ACM Symposium on Operating System Principles (SOSP), St. -
Specfp Benchmark Disclosure
SPEC CFP2006 Result Copyright 2006-2016 Standard Performance Evaluation Corporation Cisco Systems SPECfp2006 = Not Run Cisco UCS C220 M4 (Intel Xeon E5-2667 v4, 3.20 GHz) SPECfp_base2006 = 125 CPU2006 license: 9019 Test date: Mar-2016 Test sponsor: Cisco Systems Hardware Availability: Mar-2016 Tested by: Cisco Systems Software Availability: Dec-2015 0 30.0 60.0 90.0 120 150 180 210 240 270 300 330 360 390 420 450 480 510 540 570 600 630 660 690 720 750 780 810 840 900 410.bwaves 572 416.gamess 43.1 433.milc 72.5 434.zeusmp 225 435.gromacs 63.4 436.cactusADM 894 437.leslie3d 427 444.namd 31.6 447.dealII 67.6 450.soplex 48.3 453.povray 62.2 454.calculix 61.6 459.GemsFDTD 242 465.tonto 52.1 470.lbm 799 481.wrf 120 482.sphinx3 91.6 SPECfp_base2006 = 125 Hardware Software CPU Name: Intel Xeon E5-2667 v4 Operating System: SUSE Linux Enterprise Server 12 SP1 (x86_64) CPU Characteristics: Intel Turbo Boost Technology up to 3.60 GHz 3.12.49-11-default CPU MHz: 3200 Compiler: C/C++: Version 16.0.0.101 of Intel C++ Studio XE for Linux; FPU: Integrated Fortran: Version 16.0.0.101 of Intel Fortran CPU(s) enabled: 16 cores, 2 chips, 8 cores/chip Studio XE for Linux CPU(s) orderable: 1,2 chips Auto Parallel: Yes Primary Cache: 32 KB I + 32 KB D on chip per core File System: xfs Secondary Cache: 256 MB I+D on chip per core System State: Run level 3 (multi-user) Continued on next page Continued on next page Standard Performance Evaluation Corporation [email protected] Page 1 http://www.spec.org/ SPEC CFP2006 Result Copyright 2006-2016 Standard Performance -
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. -
Intel Corporation: Lenovo T400 (Intel Core 2 Duo T9900)
SPEC CFP2006 Result spec Copyright 2006-2014 Standard Performance Evaluation Corporation Intel Corporation SPECfp2006 = 21.2 Lenovo T400 (Intel Core 2 Duo T9900) SPECfp_base2006 = 20.5 CPU2006 license: 13 Test date: Mar-2009 Test sponsor: Intel Corporation Hardware Availability: Mar-2009 Tested by: Intel Corporation Software Availability: Nov-2008 0 1.00 3.00 5.00 7.00 9.00 11.0 13.0 15.0 17.0 19.0 21.0 23.0 25.0 27.0 29.0 32.0 27.6 410.bwaves 27.6 19.9 416.gamess 19.1 18.8 433.milc 18.8 434.zeusmp 24.1 19.7 435.gromacs 19.3 30.7 436.cactusADM 30.0 437.leslie3d 17.5 16.2 444.namd 16.2 24.5 447.dealII 22.1 17.1 450.soplex 16.9 30.4 453.povray 24.3 20.0 454.calculix 20.0 16.3 459.GemsFDTD 16.5 19.2 465.tonto 17.5 470.lbm 15.3 21.2 481.wrf 21.2 30.8 482.sphinx3 31.0 SPECfp_base2006 = 20.5 SPECfp2006 = 21.2 Hardware Software CPU Name: Intel Core 2 Duo T9900 Operating System: Windows XP Professional w/ SP2 (64-bit) CPU Characteristics: Compiler: Intel C++ Compiler Professional 11.0 for IA32 CPU MHz: 3066 Build 20080930 Package ID: w_cproc_p_11.0.054 Intel Visual Fortran Compiler Professional 11.0 FPU: Integrated for IA32 CPU(s) enabled: 2 cores, 1 chip, 2 cores/chip Build 20080930 Package ID: w_cprof_p_11.0.054 CPU(s) orderable: 1 chip Microsoft Visual Studio 2008 (for libraries) Primary Cache: 32 KB I + 32 KB D on chip per core Auto Parallel: Yes Secondary Cache: 6 MB I+D on chip per chip File System: NTFS Continued on next page Continued on next page Standard Performance Evaluation Corporation [email protected] Page 1 http://www.spec.org/