COSC 3406: Computer Organization Lecture 3: Computer Abstraction Kalpdrum Passi Fall 2016 ( www.cs.laurentian.ca/kpassi/cosc3406.html )

Morgan Kaufmann Publishers Computer Abstractions and Technology Chapter 1 — Computer Abstractions and Technology September 6, 2018 Morgan Kaufmann Publishers Chapter 1 Computer Abstractions and Technology

Instruction Count and CPI Chapter 1 — Computer Abstractions and Technology September 6, 2018 Morgan Kaufmann Publishers Instruction Count and CPI Instruction Count for a program Determined by program, ISA and compiler Average cycles per instruction Determined by CPU hardware If different instructions have different CPI Average CPI affected by instruction mix

Morgan Kaufmann Publishers September 6, 2018 Chapter 1 — Computer Abstractions and Technology CPI Example Computer A: Cycle Time = 250ps, CPI = 2.0 Computer B: Cycle Time = 500ps, CPI = 1.2 Same ISA Which is faster, and by how much? A is faster… …by this much

Morgan Kaufmann Publishers September 6, 2018 Chapter 1 — Computer Abstractions and Technology CPI in More Detail If different instruction classes take different numbers of cycles Weighted average CPI Relative frequency

Morgan Kaufmann Publishers Chapter 1 — Computer Abstractions and Technology Morgan Kaufmann Publishers September 6, 2018 CPI Example Alternative compiled code sequences using instructions in classes A, B, C Class A B C CPI for class 1 2 3 IC in sequence 1 IC in sequence 2 4 Sequence 1: IC = 5 Clock Cycles = 2×1 + 1×2 + 2×3 = 10 Avg. CPI = 10/5 = 2.0 Sequence 2: IC = 6 Clock Cycles = 4×1 + 1×2 + 1×3 = 9 Avg. CPI = 9/6 = 1.5

Morgan Kaufmann Publishers Chapter 1 — Computer Abstractions and Technology September 6, 2018 Performance Summary The BIG Picture Performance depends on Algorithm: affects IC, possibly CPI Programming language: affects IC, CPI Compiler: affects IC, CPI Instruction set architecture: affects IC, CPI, Tc

Morgan Kaufmann Publishers Chapter 1 — Computer Abstractions and Technology September 6, 2018 Power Trends §1.7 The Power Wall Power provides a limit to what we can cool, In the post-PC era the really valuable resource is energy. Dominant technology for IC is CMOS (complementary metal oxide semiconductor)

Power Trends The primary source of energy consumption is so-called dynamic energy—that is, energy that is consumed when transistors switch states from 0 to 1 and vice versa. The dynamic energy depends on the capacitive loading of each transistor and the voltage applied: This equation is the energy of a pulse during the logic transition of 0 → 1 → 0 or 1 → 0 → 1. The power required per transistor is just the product of energy of a transition and the frequency of transitions: ×30 5V → 1V ×1000

Morgan Kaufmann Publishers Chapter 1 — Computer Abstractions and Technology September 6, 2018 Morgan Kaufmann Publishers Reducing Power Suppose a new CPU has 85% of capacitive load of old CPU 15% voltage and 15% frequency reduction The power wall We can’t reduce voltage further We can’t remove more heat How else can we improve performance?

Uniprocessor Performance Chapter 1 — Computer Abstractions and Technology September 6, 2018 Morgan Kaufmann Publishers Uniprocessor Performance §1.8 The Sea Change: The Switch to Multiprocessors Constrained by power, instruction-level parallelism, memory latency

Morgan Kaufmann Publishers Chapter 1 — Computer Abstractions and Technology September 6, 2018 Morgan Kaufmann Publishers 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

Morgan Kaufmann Publishers Chapter 1 — Computer Abstractions and Technology September 6, 2018 Morgan Kaufmann Publishers SPEC CPU Benchmark Programs used to measure performance Supposedly typical of actual workload System 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 Dividing the execution time of a reference processor by the execution time of the evaluated computer normalizes the execution time measurements; This normalization yields a measure, called the SPECratio SPECratio is the inverse of execution time.

Morgan Kaufmann Publishers Chapter 1 — Computer Abstractions and Technology September 6, 2018 Morgan Kaufmann Publishers CINT2006 for Intel Core i7 920 A CINT2006 (integer) or CFP2006 (floating-point) Summary measurement is obtained by taking the geometric mean of SPECratios

Morgan Kaufmann Publishers Chapter 1 — Computer Abstractions and Technology September 6, 2018 Morgan Kaufmann Publishers SPEC Power Benchmark Power consumption of servers at different workload levels, divided into 10% increments, over a period of time. SPECpower started with another SPEC benchmark for Java business applications (SPECJBB2005), It exercises the processors, caches, and main memory as well as the Java virtual machine, compiler, garbage collector, and pieces of the operating system. Performance is measured in throughput, and the units are business operations per second. where ssj_opsi is performance at each 10% increment and poweri is power consumed at each performance level Power: Watts (Joules/sec)

SPECpower_ssj2008 for Xeon X5650 Chapter 1 — Computer Abstractions and Technology September 6, 2018 Morgan Kaufmann Publishers SPECpower_ssj2008 for Xeon X5650

Morgan Kaufmann Publishers Chapter 1 — Computer Abstractions and Technology September 6, 2018 Morgan Kaufmann Publishers Pitfall: Amdahl’s Law Improving an aspect of a computer and expecting a proportional improvement in overall performance §1.10 Fallacies and Pitfalls Example: multiply accounts for 80s/100s How much improvement in multiply performance to get 5× overall? Can’t be done! Corollary: make the common case fast

Fallacy: Low Power at Idle Chapter 1 — Computer Abstractions and Technology September 6, 2018 Morgan Kaufmann Publishers Fallacy: Low Power at Idle Look back at i7 power benchmark At 100% load: 258W At 50% load: 170W (66%) At 10% load: 121W (47%) 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 If future servers used, say, 10% of peak power at 10% workload, we could reduce the electricity bill of datacenters

Fallacy: Designing for performance and designing for energy efficiency unrelated Energy is power over time Hardware or software optimizations that take less time save energy overall even if the optimization takes a bit more energy when it is used.

Pitfall: MIPS as a Performance Metric Chapter 1 — Computer Abstractions and Technology September 6, 2018 Morgan Kaufmann Publishers Pitfall: MIPS as a Performance Metric MIPS: Millions of Instructions Per Second Doesn’t account for Differences in ISAs between computers Differences in complexity between instructions CPI varies between programs on a given CPU and so does MIPS

Morgan Kaufmann Publishers Chapter 1 — Computer Abstractions and Technology September 6, 2018 Morgan Kaufmann Publishers 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 Individually the factors do not determine performance: only the product, is a reliable measure of performance. §1.9 Concluding Remarks

Concluding Remarks Two of the key ideas are Power is a limiting factor exploiting parallelism in the program, via multiple processors, and exploiting locality of accesses to a memory hierarchy, typically via caches. Power is a limiting factor Use parallelism to improve performance Computer designs measured by cost and performance, as well as energy, dependability, cost of ownership, and scalability.

The University of Adelaide, School of Computer Science Chapter 2 — Instructions: Language of the Computer 6 September 2018 The University of Adelaide, School of Computer Science Chapter 2 Instructions: Language of the Computer

The University of Adelaide, School of Computer Science Chapter 2 — Instructions: Language of the Computer 6 September 2018 Instruction Set §2.1 Introduction The repertoire of instructions of a computer Different computers have different instruction sets But with many aspects in common Early computers had very simple instruction sets Simplified implementation Many modern computers also have simple instruction sets

The ARMv8 Instruction Set Chapter 2 — Instructions: Language of the Computer 6 September 2018 The University of Adelaide, School of Computer Science The ARMv8 Instruction Set A subset, called LEGv8, used as the example throughout the book Commercialized by ARM Holdings (www.arm.com) Large share of embedded core market Applications in consumer electronics, network/storage equipment, cameras, printers, … Typical of many modern ISAs See ARM Reference Data tear-out card

Arithmetic Operations Chapter 2 — Instructions: Language of the Computer 6 September 2018 The University of Adelaide, School of Computer Science Arithmetic Operations Add and subtract, three operands Two sources and one destination ADD a, b, c // a gets b + c All arithmetic operations have this form Each LEGv8 arithmetic instruction performs only one operation and must always have exactly three variables Design Principle 1: Simplicity favours regularity Regularity makes implementation simpler Simplicity enables higher performance at lower cost §2.2 Operations of the Computer Hardware

The University of Adelaide, School of Computer Science Chapter 2 — Instructions: Language of the Computer 6 September 2018 The University of Adelaide, School of Computer Science Arithmetic Example Compiling Two C Assignment Statements into LEGv8: a = b +c ; d = a – e ; Compiled LEGv8 code: ADD a, b, c SUB d, a, e Compiling a Complex C Assignment into LEGv8: f = (g + h) - (i + j); ADD t0, g, h // temp t0 = g + h ADD t1, i, j // temp t1 = i + j SUB f, t0, t1 // f = t0 - t1