CIS775: Computer Architecture

1. CIS775: Computer Architecture Chapter 1: Fundamentals of Computer Design

2. Course Objectives • To evaluate the issues involved in choosing and designing instruction set. • To learn concepts behind advanced pipelining techniques. • To understand the “hitting the memory wall” problem and the current state-of-art in memory system design. • To understand the qualitative and quantitative tradeoffs in the design of modern computer systems

3. What is Computer Architecture? • Functional operation of the individual HW units within a computer system, and the flow of information and control among them. Programming Parallelism Technology Language Interface Computer Architecture: Interface Design (ISA) Hardware Organization OS Applications Measurement & Evaluation

4. Computer Architecture Topics Input/Output and Storage Disks, WORM, Tape RAID Emerging Technologies Interleaving Memories DRAM Coherence, Bandwidth, Latency Memory Hierarchy L2 Cache L1 Cache Addressing, Protection, Exception Handling VLSI Instruction Set Architecture Pipelining and Instruction Level Parallelism Pipelining, Hazard Resolution, Superscalar, Reordering, Prediction, Speculation, Vector, DSP

5. Computer Architecture Topics Shared Memory, Message Passing, Data Parallelism P M P M P M P M ° ° ° Network Interfaces S Interconnection Network Processor-Memory-Switch Topologies, Routing, Bandwidth, Latency, Reliability Multiprocessors Networks and Interconnections

6. Measurement and Evaluation • Architecture is an iterative process: • Searching the space of possible designs • At all levels of computer systems Creativity Cost / Performance Analysis Good Ideas Mediocre Ideas Bad Ideas

7. Issues for a Computer Designer • Functional Requirements Analysis (Target) • Scientific Computing – HiPerf floating pt. • Business – transactional support/decimal arith. • General Purpose –balanced performance for a range of tasks • Level of software compatibility • PL level • Flexible, Need new compiler, portability an issue • Binary level (x86 architecture) • Little flexibility, Portability requirements minimal • OS requirements • Address space issues, memory management, protection • Conformance to Standards • Languages, OS, Networks, I/O, IEEE floating pt.

8. 1988 Supercomputers Massively Parallel Processors Mini-supercomputers Minicomputers Workstations PC’s 2002 Powerful PC’s and SMP Workstations Network of SMP Workstations Mainframes Supercomputers Embedded Computers Computer Systems: Technology Trends

9. Why Such Change in 10 years? • Performance • Technology Advances • CMOS (complementary metal oxide semiconductor) VLSI dominates older technologies like TTL (transistor transistor logic) in cost AND performance • Computer architecture advances improves low-end • RISC, pipelining, superscalar, RAID, … • Price: Lower costs due to … • Simpler development • CMOS VLSI: smaller systems, fewer components • Higher volumes • Lower margins by class of computer, due to fewer services • Function :Rise of networking/local interconnection technology

10. Growth in Microprocessor Performance

11. Six Generations of DRAMs

12. Updated Technology Trends(Summary) Capacity Speed (latency) Logic 4x in 4 years 2x in 3 years DRAM 4x in 3 years 2x in 10 years Disk 4x in 2 years 2x in 10 years Network (bandwidth) 10x in 5 years • Updates during your study period?? • BS (4 yrs) • MS (2 yrs) • PhD (5 yrs)

15. Performance Trends(Summary) • Workstation performance (measured in Spec Marks) improves roughly 50% per year (2X every 18 months) • Improvement in cost performance estimated at 70% per year

16. Computer Engineering Methodology Evaluate Existing Systems for Bottlenecks Implementation Complexity Benchmarks Technology Trends Implement Next Generation System Simulate New Designs and Organizations Workloads

17. DC to Paris Speed Passengers Throughput (pmph) 6.5 hours 610 mph 470 286,700 3 hours 1350 mph 132 178,200 How to Quantify Performance? Plane Boeing 747 BAD/Sud Concodre • Time to run the task (ExTime) • Execution time, response time, latency • Tasks per day, hour, week, sec, ns … (Performance) • Throughput, bandwidth

18. The Bottom Line: Performance and Cost or Cost and Performance? • "X is n times faster than Y" means • ExTime(Y) Performance(X) • --------- = --------------- • ExTime(X) Performance(Y) • Speed of Concorde vs. Boeing 747 • Throughput of Boeing 747 vs. Concorde • Cost is also an important parameter in the equation which is why concordes are being put to pasture!

19. Measurement Tools • Benchmarks, Traces, Mixes • Hardware: Cost, delay, area, power estimation • Simulation (many levels) • ISA, RT, Gate, Circuit • Queuing Theory • Rules of Thumb • Fundamental “Laws”/Principles • Understanding the limitations of any measurement tool is crucial.

20. Metrics of Performance Application Answers per month Operations per second Programming Language Compiler (millions) of Instructions per second: MIPS (millions) of (FP) operations per second: MFLOP/s ISA Datapath Megabytes per second Control Function Units Cycles per second (clock rate) Transistors Wires Pins

21. Cases of Benchmark Engineering • The motivation is to tune the system to the benchmark to achieve peak performance. • At the architecture level • Specialized instructions • At the compiler level (compiler flags) • Blocking in Spec89  factor of 9 speedup • Incorrect compiler optimizations/reordering. • Would work fine on benchmark but not on other programs • I/O level • Spec92 spreadsheet program (sp) • Companies noticed that the produced output was always out put to a file (so they stored the results in a memory buffer) and then expunged at the end (which was not measured). • One company eliminated the I/O all together.

22. After putting in a blazing performance on the benchmark test, Sun issued a glowing press release claiming that it had outperformed Windows NT systems on the test. Pendragon president Ivan Phillips cried foul, saying the results weren't representative of real-world Java performance and that Sun had gone so far as to duplicate the test's code within Sun's Just-In-Time compiler. That's cheating, says Phillips, who claims that benchmark tests and real-world applications aren't the same thing. Did Sun issue a denial or a mea culpa? Initially, Sun neither denied optimizing for the benchmark test nor apologized for it. "If the test results are not representative of real-world Java applications, then that's a problem with the benchmark," Sun's Brian Croll said. After taking a beating in the press, though, Sun retreated and issued an apology for the optimization.[Excerpted from PC Online 1997]

23. Issues with Benchmark Engineering • Motivated by the bottom dollar, good performance on classic suites  more customers, better sales. • Benchmark Engineering  Limits the longevity of benchmark suites • Technology and Applications Limits the longevity of benchmark suites.

24. SPEC: System Performance Evaluation Cooperative • First Round 1989 • 10 programs yielding a single number (“SPECmarks”) • Second Round 1992 • SPECInt92 (6 integer programs) and SPECfp92 (14 floating point programs) • Compiler Flags unlimited. March 93 • new set of programs: SPECint95 (8 integer programs) and SPECfp95 (10 floating point) • “benchmarks useful for 3 years” • Single flag setting for all programs: SPECint_base95, SPECfp_base95 • SPEC CPU2000 (11 integer benchmarks – CINT2000, and 14 floating-point benchmarks – CFP2000

25. SPEC 2000 (CINT 2000)Results

26. SPEC 2000 (CFP 2000)Results

27. Reporting Performance Results • Reproducability •  Apply them on publicly available benchmarks. Pecking/Picking order • Real Programs • Real Kernels • Toy Benchmarks • Synthetic Benchmarks

28. How to Summarize Performance • Arithmetic mean (weighted arithmetic mean) tracks execution time: sum(Ti)/n or sum(Wi*Ti) • Harmonic mean (weighted harmonic mean) of rates (e.g., MFLOPS) tracks execution time: n/sum(1/Ri) or 1/sum(Wi/Ri) • Normalized execution time is handy for scaling performance (e.g., X times faster than SPARCstation 10) • But do not take the arithmetic mean of normalized execution time, use the geometric mean = (Product(Ri)^1/n)

29. Performance Evaluation • “For better or worse, benchmarks shape a field” • Good products created when have: • Good benchmarks • Good ways to summarize performance • Given sales is a function in part of performance relative to competition, investment in improving product as reported by performance summary • If benchmarks/summary inadequate, then choose between improving product for real programs vs. improving product to get more sales;Sales almost always wins! • Execution time is the measure of computer performance!

30. Simulations • When are simulations useful? • What are its limitations, I.e. what real world phenomenon does it not account for? • The larger the simulation trace, the less tractable the post-processing analysis.

31. Queueing Theory • What are the distributions of arrival rates and values for other parameters? • Are they realistic? • What happens when the parameters or distributions are changed?

32. Quantitative Principles of Computer Design • Make the Common Case Fast • Amdahl’s Law • CPU Performance Equation • Clock cycle time • CPI • Instruction Count • Principles of Locality • Take advantage of Parallelism

34. Amdahl’s Law ExTimenew = ExTimeold x (1 - Fractionenhanced) + Fractionenhanced Speedupenhanced 1 ExTimeold ExTimenew Speedupoverall = = (1 - Fractionenhanced) + Fractionenhanced Speedupenhanced

35. Amdahl’s Law • Floating point instructions improved to run 2X; but only 10% of actual instructions are FP ExTimenew= Speedupoverall =

36. CPU time = Seconds = Instructions x Cycles x Seconds Program Program Instruction Cycle CPU Performance Equation Inst Count CPI Clock Rate Program X Compiler X (X) Inst. Set. X X Organization X X Technology X

37. Cycles Per Instruction “Average Cycles per Instruction” • CPI = (CPU Time * Clock Rate) / Instruction Count • = Cycles / Instruction Count Invest Resources where time is Spent! n CPU time = CycleTime *  CPI * I i i i = 1 “Instruction Frequency” n CPI =  CPI * F where F = I i i i i i = 1 Instruction Count

38. Example: Calculating CPI Base Machine (Reg / Reg) Op Freq Cycles CPI(i) (% Time) ALU 50% 1 .5 (33%) Load 20% 2 .4 (27%) Store 10% 2 .2 (13%) Branch 20% 2 .4 (27%) 1.5 Typical Mix

39. Chapter Summary, #1 • Designing to Last through Trends • Capacity Speed • Logic 2x in 3 years 2x in 3 years • DRAM 4x in 3 years 2x in 10 years • Disk 4x in 3 years 2x in 10 years • 6yrs to graduate => 16X CPU speed, DRAM/Disk size • Time to run the task • Execution time, response time, latency • Tasks per day, hour, week, sec, ns, … • Throughput, bandwidth • “X is n times faster than Y” means • ExTime(Y) Performance(X) • --------- = -------------- • ExTime(X) Performance(Y)

40. 1 ExTimeold ExTimenew Speedupoverall = = (1 - Fractionenhanced) + Fractionenhanced Speedupenhanced CPU time = Seconds = Instructions x Cycles x Seconds Program Program Instruction Cycle Chapter Summary, #2 • Amdahl’s Law: • CPI Law: • Execution time is the REAL measure of computer performance! • Good products created when have: • Good benchmarks, good ways to summarize performance • Die Cost goes roughly with die area4

41. Food for thought • Two companies reports results on two benchmarks one on a Fortran benchmark suite and the other on a C++ benchmark suite. • Company A’s product outperforms Company B’s on the Fortran suite, the reverse holds true for the C++ suite. Assume the performance differences are similar in both cases. • Do you have enough information to compare the two products. What information will you need?

42. Food for Thought II • In the CISC vs. RISC debate a key argument of the RISC movement was that because of its simplicity, RISC would always remain ahead. • If there were enough transistors to implement a CISC on chip, then those same transistors could implement a pipelined RISC • If there was enough to allow for a pipelined CISC there would be enough to have an on-chip cache for RISC. And so on. • After 20 years of this debate what do you think? • Hint: Think of commercial PC’s, Moore’s law and some of the data in the first chapter of the book (and on these slides)

43. Amdahl’s Law (answer) • Floating point instructions improved to run 2X; but only 10% of actual instructions are FP ExTimenew= ExTimeold x (0.9 + .1/2) = 0.95 x ExTimeold 1 Speedupoverall = = 1.053 0.95