Chapter 1: Fundamentals of Quantitative Design and Analysis

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1 1 / 12 Chapter 1: Fundamentals of Quantitative Design and Analysis Be careful in this chapter. It contains a tremendous amount of information and data about the changes in computer architecture since the early 80 s. Every time I read this chapter it triggers thoughts and reminders of key developments in computer systems. I encourage you to study it carefully and revisit it from time to time in your future years of study.

2 2 / 12 Chapter Overview Classes of Computers Defining Computer Architecture Trends Measuring, Reporting and Summarizing Performance Principles of Quantitative Design Putting it all together

3 3 / 12 Growth in Processor Performance 100,000 Performance (vs. VAX-11/780) 10, AX-11/780, 5 MHz Intel Xeon 6 cores, 3.3 GHz (boost to 3.6 GHz) Intel Xeon 4 cores, 3.3 GHz (boost to 3.6 GHz) Intel Core i7 Extreme 4 cores 3.2 GHz (boost to 3.5 GHz) 24,129 Intel Core Duo Extreme 2 cores, 3.0 GHz 21,871 Intel Core 2 Extreme 2 cores, 2.9 GHz AMD Athlon 64, 2.8 GHz 11,865 14,38719,484 AMD Athlon, 2.6 GHz Intel Xeon EE 3.2 GHz 7,108 Intel D850EMVR motherboard (3.06 GHz, Pentium 4 processor with Hyper-Threading Technology) 6,043 6,681 IBM Power4, 1.3 GHz 4,195 Intel VC820 motherboard, 1.0 GHz Pentium III processor 3,016 Professional Workstation XP1000, 667 MHz 21264A 1,779 Digital AlphaServer /575, 575 MHz , AlphaServer /600, 600 MHz Digital Alphastation 5/500, 500 MHz Digital Alphastation 5/300, 300 MHz %/year Digital Alphastation 4/266, 266 MHz 183 IBM POWERstation 100, 150 MHz 117 Digital 3000 AXP/500, 150 MHz 80 HP 9000/750, 66 MHz 51 IBM RS6000/540, 30 MHz 24 52%/year MIPS M2000, 25 MHz MIPS M/120, 16.7 MHz Sun-4/260, 16.7 MHz 9 VAX 8700, 22 MHz 5 25%/year 1.5, VAX-11/

4 4 / 12 Observations of Processor Growth RISC & Chip transistor count accelerate growth curve Transistor count facilitate translation of x86 Since 2003 power and ILP limits negatively impact growth Transition from ILP to: data-level parallelism (DLP) thread-level parallelism (TLP) request-level parallelism (RLP)

5 5 / 12 Classes of Computers Personal mobile devices (PMD): cost, energy, media perf, responsiveness Desktop: price-performance, energy, graphics perf Server: throughput, availability, scalability, energy Warehouse: price-performance, throughput, energy Embedded: price, energy application-targeted performance

6 6 / 12 Parallelism: Application/Architecture Application: data-/task-level parallelism (DLP, TLP) Architecture: Instruction Level Parallelism (ILP) Vector Machines/Graphics Processing Units (GPUs) Thread-level parallelism Request-level parallelism

7 7 / 12 Defining Computer Architecture Architecture: Covering all aspects of computer design instruction set architecture, organization (or micro-architecture), and hardware. Overall the role of the architect is to design a computer (or system) to meet functional requirements and performance requirements where performance includes considerations of all significant requirements including price, power, etc.

8 8 / 12 Trends Technology: transistor density, memory, disk, and network Bandwidth and latency: trade latency for bandwidth often works Power and Energy: careful here Do nothing well Cost Dynamic Voltage-Frequency Scaling (DVFS) Design for the typical case Overclocking/turbo mode

9 9 / 12 Measuring and Reporting Performance Execution time is king. But what is execution time : wall-clock, time in CPU? Benchmarks: kernels, toy programs, synthetic benchmarks Dhrystone SPEC, TPC Reproducibility

10 10 / 12 Principles of Quantitative Design Locality: 90/10 rule Focus on the common case: drive evaluation of functional and performance requirements in this space. Amdahl s Law: Speedup overall = Execution time old Execution time new = 1 (1 Fraction enhanced ) + Fraction enhanced Speedup enhanced

11 11 / 12 Putting it all together Remember it is ultimately the system performance that matters. Optimizing one item and ignoring other (sometimes dominate) subsystems will easily lead to failure. For example, the CPU core is only one (often small) factor in the power budget.

12 Some Key Transition Points in Computing 1. D. E. Knuth, An empirical study of FORTRAN programs, Software: Practice and Experience, vol 1, no 2, , M. G. H. Katevenis, Reduced Instruction Set Computer Architectures for VLSI, The MIT Press, Possibly not as fundamental as the two above, the next two are, nevertheless indicating the clear turning point toward multi-/many-core processing: 3.1 A. Ghuloum, Face the Inevitable, Embrace Parallelism, Communications of the ACM, vol 52, no 9, 36 38, Sep K. Asanovic et al, A View of the Parallel Computing Landscape, Communications of the ACM, vol 52, no 10, 56 67, Oct And, of course, the textbook for this course. 5. Things to watch: 5.1 H. Esmaeilzadeh, E. Bleem, R. St Amant, K. Sankaralingam, D. Burger, Dark Silicon and the End of Multicore Scaling, Proc of the 38th Int Symp on Computer Architecture (ISCA 11), JUne / 12

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