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Code Optimization

This lecture discusses essential code optimization techniques crucial for improving program performance in embedded systems. Key concepts include better data structures and algorithms, distinctions between merge and bubble sorts, and strategies like dead code elimination, loop unrolling, and in-lining. The agenda covers possible exam ideas while emphasizing that reconfigurable processors aren't universal, hence requiring programmers' intervention. It draws heavily from work by Profs. Raj Rajkumar and Priya Narasimhan at Carnegie Mellon University, ensuring a solid foundation for practical applications.

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Code Optimization

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  1. Code Optimization Witawas Srisa-an CSCE 496: Embedded Systems Design and Implementation

  2. Agenda • Talk about possible exam ideas • Code optimization techniques • Not everyone has reconfigurable processors! • Credits • Most of slides in this lecture are based on slides created by Profs. Raj Rajkumar and Professor Priya Narasimhan from ECE Dept at Carnegie Mellon

  3. Exam Ideas

  4. Code Optimization • Programmers can improve program performance by writing better code • Improve data structure and/or algorithms • Merge vs. bubble sorts • Reorganize code or provide flags to help compilers • Last option is to write in assembly

  5. Better Algorithms • Merge vs. bubble sorts • Which one runs faster? • Which one causes more cache misses?

  6. Common Optimization Techniques • Sub-expression elimination • Dead code elimination • Induction variables • Strength reduction • Loop unrolling • In-lining

  7. Common Techniques (cont.) • Sub-expression elimination myfunction: index1 = 8 * i x = a [index1] temp = 8 * i index2 = 4 * j t = a[index2] a[temp] = t temp2 = 4 * j a[temp2] = x goto myfunction

  8. Common Techniques (cont.) • Dead code elimination int i = 0; i = i + 1; if (i == 0) j = j * 8; else j = j * 10; use ASSERT and #ifdef to advice the compiler about deadcode

  9. Common Techniques (cont.) • Induction variables and strength reduction i = 0 j = 0 label j = j + 1 i = 4 * j a[i * 2] = b [i] if (i < 1000) goto label

  10. Optimization Techniques (cont.) • In-lining main: addi $s0, $t1, 0 addi $s1, $t2, 0 jal mult add $t3, $v0, 0 mult: addi $sp, $sp -12 sw $s1, 4($sp) sw $s0, 8($sp) sw $ra, 12($sp) sll $v0, $s0, $s1 lw $s1, 4($sp) lw $s0, 8($sp) lw $ra, 12($sp) addi $sp, $sp, 12 jr $ra What’s wrong with this picture?

  11. Optimization Techniques (cont.) • Loop unrolling • Eliminate branches (why?)

  12. Architecture Dependent Optimizations X = Y * 64 Convert 8-bit RGB to 8-bit YCC Y = 0.299R + 0.587G + 0.114B Cb = -0.169R - 0.331G + 0.500B + 128 Cr = 0.500R - 0.419G – 0.082B + 128

  13. Architecture Dependent Optimizations (cont.) Address Register Addr Incrementer Incrementer Bus ALU Bus Register Bank Write Buffer (holds address and data) A Bus Barrel Shifter B Bus 32-bit ALU Mem Addr Register Write Data Register Read Data/Instr Reg Dout[31:0] Data[31:0] RAM

  14. Summary • No magic bullet • optimizations sometimes don’t work • programmers need to help • various techniques that may require prior knowledge of the hardware

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