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Deoptimizing a program for the pipeline in Intel Sandybridge-family CPUs
Home Backend Development C++ How Can a Monte Carlo Simulation Be Further Deoptimized to Significantly Slow Down Execution on an Intel Sandybridge-Family CPU?

How Can a Monte Carlo Simulation Be Further Deoptimized to Significantly Slow Down Execution on an Intel Sandybridge-Family CPU?

Nov 29, 2024 am 12:57 AM

How Can a Monte Carlo Simulation Be Further Deoptimized to Significantly Slow Down Execution on an Intel Sandybridge-Family CPU?

Deoptimizing a program for the pipeline in Intel Sandybridge-family CPUs

Goal: To pessimize a program to run slower, by exploiting knowledge of the Intel i7 pipeline.

Problem:

The assignment provided two options: Whetstone or Monte-Carlo programs. The student chose the Monte-Carlo simulation program, but their pessimization efforts only increased the code running time by a second.

Question:

How can the student further pessimize the code to achieve a more significant slowdown?

Answer:

General Strategies:

  • Introduce unpredictable branches to increase mispredict penalties.
  • Lengthen loop-carried dependency chains to reduce instruction-level parallelism.
  • Use slower FP operations and divs, especially exp and log functions.

Uarch-Specific Ideas:

With intrinsics ():

  • Use movnti to evict data from cache.
  • Use integer shuffles between FP math operations to cause bypass delays.
  • Avoid mixing SSE and AVX instructions without using vzeroupper.

With (inline) asm:

  • Force alignment issues to break the uop cache.
  • Use self-modifying code to trigger pipeline clears.

Inducing Cache Misses and Memory Slowdowns:

  • Perform narrow stores to cause store-forwarding stalls.
  • Replace local vars with members of a big struct to control memory layout.
  • Arrange memory layout to increase cache misses and page-split loads.
  • Use misaligned variables to span cache-line or page boundaries.
  • Loop over arrays in non-contiguous order.
  • Consider using linked lists instead of arrays.

Other Techniques:

  • Use std::atomic loop counters for slower atomic operations.
  • Compile with -m32 or -march=i386 to force slower code generation.
  • Force lower-precision long double calculations for extra slowness.
  • Frequently set CPU affinity to different CPUs.
  • Implement excessive system calls for context switching overhead.

Final Notes:

  • While these techniques effectively slow down the code, their level of "diabolical incompetence" depends on the justification given.
  • The assignment instructor may have intended for students to learn about pipeline hazards and dependencies, rather than merely applying these techniques blindly.

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