CPS3340 COMPUTER ARCHITECTURE Fall Semester, /03/2013 Lecture 3: Computer Performance Instructor: Ashraf Yaseen DEPARTMENT OF MATH & COMPUTER SCIENCE CENTRAL STATE UNIVERSITY, WILBERFORCE, OH 1 Review Last Class Definition of Computer Performance Measure of Computer Performance This Class Computer Performance Power Wall Assignment 1 Next Class Computer Logic Boolean 2 Performance Summary Performance depends on Algorithm: affects IC, possibly CPI Programming language: affects IC, CPI Compiler: affects IC, CPI Instruction set architecture: affects IC, CPI, T c The BIG Picture 3 Power Trends In CMOS IC technology 1.5 The Power Wall 1000 30 5V 1V 4 Reducing Power Suppose a new CPU has 85% of capacitive load of old CPU 15% voltage and 15% frequency reduction The power wall We cant reduce voltage further We cant remove more heat How else can we improve performance? 5 Uniprocessor Performance 1.6 The Sea Change: The Switch to Multiprocessors Constrained by power, instruction-level parallelism, memory latency 6 Multiprocessors Multicore microprocessors More than one processor per chip Requires explicitly parallel programming Compare with instruction level parallelism Hardware executes multiple instructions at once Hidden from the programmer Hard to do Programming for performance Load balancing Optimizing communication and synchronization 7 Manufacturing ICs Yield: proportion of working dies per wafer 1.7 Real Stuff: The AMD Opteron X4 8 AMD Opteron X2 Wafer X2: 300mm wafer, 117 chips, 90nm technology X4: 45nm technology 9 Integrated Circuit Cost Nonlinear relation to area and defect rate Wafer cost and area are fixed Defect rate determined by manufacturing process Die area determined by architecture and circuit design 10 SPEC CPU Benchmark Programs used to measure performance Supposedly typical of actual workload Standard Performance Evaluation Cooperative (SPEC) Develops benchmarks for CPU, I/O, Web, SPEC CPU2006 Elapsed time to execute a selection of programs Negligible I/O, so focuses on CPU performance Normalize relative to reference machine Summarize as geometric mean of performance ratios CINT2006 (integer) and CFP2006 (floating-point) 11 CINT2006 for Opteron X NameDescriptionIC10 9 CPITc (ns)Exec timeRef timeSPECratio perlInterpreted string processing2, , bzip2Block-sorting compression2, , gccGNU C Compiler1, , mcfCombinatorial optimization ,3459, goGo game (AI)1, , hmmerSearch gene sequence2, , sjengChess game (AI)2, , libquantumQuantum computer simulation1, ,04720, h264avcVideo compression3, , omnetppDiscrete event simulation , astarGames/path finding1, , xalancbmkXML parsing1, ,1436, Geometric mean SPEC Power Benchmark Power consumption of server at different workload levels Performance: ssj_ops/sec Power: Watts (Joules/sec) 13 SPECpower_ssj2008 for X4 Target Load %Performance (ssj_ops/sec)Average Power (Watts) 100%231, %211, %185, %163, %140, %118, %920, %70, %47, %23, %0141 Overall sum1,283,5902,605 ssj_ops/ power493 14 Pitfall: Amdahls Law Improving an aspect of a computer and expecting a proportional improvement in overall performance 1.8 Fallacies and Pitfalls Cant be done! Example: multiply accounts for 80s/100s How much improvement in multiply performance to get 5 overall? Corollary: make the common case fast 15 Fallacy: Low Power at Idle Look back at X4 power benchmark At 100% load: 295W At 50% load: 246W (83%) At 10% load: 180W (61%) Google data center Mostly operates at 10% 50% load At 100% load less than 1% of the time Consider designing processors to make power proportional to load 16 Pitfall: MIPS as a Performance Metric MIPS: Millions of Instructions Per Second Doesnt account for Differences in ISAs between computers Differences in complexity between instructions CPI varies between programs on a given CPU 17 Concluding Remarks Cost/performance is improving Due to underlying technology development Hierarchical layers of abstraction In both hardware and software Instruction set architecture The hardware/software interface Execution time: the best performance measure Power is a limiting factor Use parallelism to improve performance 1.9 Concluding Remarks 18 Summary Performance Definition Power Trend Amdahls Law 19 What I want you to do Review Chapter 1 Work on your assignment 1 20