1. + CS 325: CS Hardware and Software Organization and Architecture Computer Evolution and Performance 2

2. + Outline Von Neumann Architecture Processor Hierarchy Registers ALU Processor Categories Processor Performance Amdahl’s Law Computer Benchmarks

3. + Von Neumann Architecture Characteristic of most modern processors. Central idea is Stored Program. Three basic components: Processor Memory I/O Facilities

4. + Illustration of Von Neumann Architecture

5. + Processor Digital Device. Performs computation involving multiple steps. Building blocks used to form computer system.

6. + Hierarchical Structure and Computational Engines Most computer architecture follows a hierarchical approach. Subparts of a large, central processor are sophisticated enough to meet our definition of a processor. Some engineers use the term computational engine for sub-piece that is less powerful than the main processor.

7. + Illustration of Processor Hierarchy

8. + Major Components of a Conventional Processor Controller Computational Engine (ALU) Local Data Storage Internal Interconnections External Interface

9. + Illustration of a Conventional Processor

10. + Parts of a Conventional Processor Controller Overall responsibility for execution Moves through sequence of steps Coordinates other units Computational Engine Operates as directed by controller Typically provides arithmetic and Boolean operations (ALU) Performs one operation at a time

11. + Parts of a Conventional Processor Local Data Storage Holds data values for operations Must be loaded before operation can be performed Typically implemented with registers Internal Interconnections Allows transfer of values among units of the processor Sometimes called data path

12. + Parts of a Conventional Processor External Interface Handles communication between processor and rest of computer system Provides connections to external memory as well as external I/O devices

13. + Another Illustration of Processor

14. + Parts of a Conventional Processor ALU Status Flags: Neg, Zero, Carry, Overflow Shifter: Left  multiplication by 2 Right  division by 2 Complementer: Logical NOT

15. + Example Register Organizations

16. + Processor Registers Motorola CPU - MC68000 8 32-bit general purpose registers (D0 – D7) 8 32-bit address registers (A0 – A7) 1 32-bit program counter 1 16 status register

17. + Processor Registers Intel 8086 – 16-bit General Purpose: AX – Accumulator: Multiply, Divide, I/O BX – Base: Pointer to base address (data) CX – Count: Counter for loops, shifts DX – Data: Multiply, Divide, I/O Pointer and Index: SP – Stack Pointer: pointer to top of stack BP – Base Pointer: pointer to base address (stack) SI – Source Index: source string/index pointer DI – Destination Index: Destination string/index pointer Segment Registers: CS – Code Segment DS – Data Segment SS – Stack Segment ES – Extra Segment Program Status: PC – Program Counter SR – Status Register

18. + Processor Registers Intel 80386 – Pentium 2 Similar to 8086, but register width doubled to 32-bit

19. + Arithmetic Logic Unit (ALU) Main computational engine in conventional processor. Complex unit that can perform variety of tasks Integer arithmetic (add, subtract, multiply, divide) Shift (left, right, circular) Boolean (AND, OR, NOT, XOR) Typically CPU “bit size” refers to ALU and register size 32-bit CPU  32-bit ALU and registers 64-bit CPU  64-bit ALU and registers

20. + Processor Categories and Roles Many possible roles for individual processors in: Coprocessors Microcontrollers Microsequencers Embedded system processors General purpose processors

21. + Coprocessor Operates in conjunction with and under the control of another processor. Special purpose processor Performs a single task Operates at high speed Example: Math Coprocessor Used for floating point mathematical operations

22. + Microcontroller Programmable device Dedicated to control of a physical system Example: ECU for automobile engine Roadway intersection traffic lights

23. + Microsequencer Similar to microcontroller Controls coprocessors and other engines within a large processor Example: Move operands to floating point unit Invoke an operation (divide) Move result back to memory

24. + Embedded System Processor Operates sophisticated electronic device Usually more powerful than microcontroller Example: Controlling a DVD player, including commands from a remote control

25. + General Purpose Processor Most powerful type of processor Completely programmable Full functionality Example: CPU in personal computer/laptop (CISC x86 architecture) CPU in smartphone/tablet (RISC ARM architecture)

26. + Processor Performance

27. + Clock and Instruction Rate Clock Cycle Time interval in which all basic circuits (steps) inside a process must complete Time at which gates are clocked (gate-signal propagation) Clock Rate 1/clock cycle (GHz – billion cycles per second) Instruction Rate Measure of time required to execute instructions MIPS – million instructions per second Varies since some instructions take more time (more clock cycles) than others Shift left instruction vs. fetch from memory instruction

28. + Basic Performance Equation Define:N = Number of instructions executed in the program S = Average number of cycles for instructions in the program R = Clock rate T = Program execution time T = N * S R

29. + Improve Performance To improve performance: Decrease N and/or S Increase R Parameters are not independent: Increasing R may increase S as well N is primarily controlled by compiler Processors with large R may not have the best performance Due to larger S Making logic circuits faster/smaller is a definite win Increases R while S and N remain unchanged

30. + Amdahl’s Law Potential speed up of program using multiple processors. Concluded that: Code needs to be parallelizable Speed up is bound, giving diminishing returns for more processors Task dependent Servers gain by maintaining multiple connections on multiple processors Databases can be split into parallel tasks

31. + Amdahl’s Law Most important principle in computer design: Make the common case fast Optimize for the normal case Enhancement: any change/modification in the design of a component Speedup: how much faster a task will execute using an enhanced component versus using the original component. Speedup = Component enhanced Component original

32. + Amdahl’s Law The enhanced feature may not be used all the time. Let the fraction of the computation time when the enhanced feature is used be F. Let the speedup when the enhanced feature is used be Se. Now the execution time with the enhancement is: Ex new = Ex old * (1 – F) + Ex old * (F/Se) This gives the overall speedup (So) as: So = Exold/Exnew = 1 / ((1 - F) + (F/Se))

33. + Amdahl’s Law – Example 1 Suppose that we are considering an enhancement that runs 10 times faster than the original component but is usable only 40% of the time. What is the overall speedup gained by incorporating the enhancement? Se = 10 F = 40 / 100 = 0.4 So = 1 / ((1 – F) + (F / Se)) = 1 / (0.6 + (0.4 / 10)) = 1 / 0.64 = 1.56

34. + Amdahl’s Law – Example 2 Suppose that we hired a guru programmer that made 70% of our program run 15x faster that the original program. What is the speedup of the enhanced program? Se = 15 F = 70 / 100 = 0.7 So = 1 / ((1 – F) + (F / Se)) = 1 / (0.3 + (0.7 / 15)) = 1 / 0.347 = 2.88

35. + Amdahl’s Law – Example 3 Suppose that we hired two students to enhance our WKU web Server performance. The first student increased the performance of the server by 12% for 85% of the time. The second student increased the performance of the server by 2x for 25% of the time. Which student produced the overall highest speedup? Student1Student2 Se = 1.12Se = 2 F = 85 / 100 = 0.85F = 25 / 100 = 0.25So = 1 / ((1 – F) + (F / Se)) = 1 / (0.15 + (0.85 / 1.12)) = 1 / (0.75 + (0.25 / 2)) = 1 / 0.909 = 1 / 0.875 = 1.1 = 1.14

36. + Benchmarks LINPACK (Scientific Computing) Speed in solving linear system of equations (matrix multiplications) http://www.top500.org/list/2013/11/

37. + Top 10 Supercomputers

38. + Top 500 Performance Development

39. + Benchmarks - LINPACK Current fastest supercomputer: Tianhe-2 (MiklyWay-2) 3.12 million cores @ 2.2Ghz 33.86 Pflops/sec = 33,860,000,000,000,000 Floating point operations/sec Current High End Desktop: Intel I7 “Haswell” 4770k 4 cores @ 3.5Ghz 177 Gflops/sec = 177,000,000,000 Floating point operations/sec Current Google Android Smartphone: Google Nexus 5 4 cores @ 2.3Ghz ARM RISC Architecture 393 Mflops/sec = 393,000,000 Floating point operations/sec