How Can We Intentionally Deoptimize a Program to Expose Intel Sandybridge Pipeline Bottlenecks?

Deoptimizing a Program for the Pipeline in Intel Sandybridge-family CPUs

Problem:

The assignment is to introduce inefficiencies into a given program to slow down its execution speed. The program is a Monte Carlo simulation using Gaussian random numbers, and the focus is on exploiting the pipeline structure of Intel Sandybridge-family CPUs.

Solution:

Introducing Inefficiencies for Pipeline Hazards:

• Use atomic operations with store-load fences: Repeated atomic operations on shared variables create memory dependencies that stall the pipeline. Additionally, store-load fences force serialized execution of memory operations.
• Create false sharing: Ensure that multiple threads access adjacent memory locations in different cache lines, causing cache bank conflicts and invalidations.
• Use irregular memory access patterns: Avoid sequential memory access to prevent efficient prefetching and cache utilization. For example, access elements in a non-contiguous order or use linked lists instead of arrays.

Weakening Loop-Level Parallelism:

• Serialize loop iterations: Use synchronization mechanisms such as locks or atomic increments to ensure only one thread executes each loop iteration at a time.
• Introduce unpredictable branches: Include branches that are difficult for the branch predictor to predict, causing mispredictions and pipeline flushes when the wrong path is taken.
• Use inefficient operations: Replace efficient arithmetic operations with slower alternatives, such as division instead of multiplication, or square root instead of multiplication and multiplication of constants instead of logarithms and exponential functions.

Exploiting Microarchitectural Features:

• Cause unnecessary register spills and fills: Use many local variables and large data structures, forcing spill and fill operations to memory.
• Use inefficient instructions: Utilize instructions that incur significant stalls or reduce instruction-level parallelism, such as unaligned memory accesses or 16-bit operations in 32-bit mode.
• Contend for cache resources: Create excessive cache misses by accessing multiple arrays or data structures simultaneously, using non-contiguous memory access patterns, or explicitly invalidating cache lines using instructions like CLFLUSH.

Compiler Optimization Avoidance:

• Use inline assembly: Bypass compiler optimizations by using inline assembly to manually control instruction generation and avoid cache-friendly code transformations.
• Use undefined behavior: Perform operations that may cause unexpected behavior or generate inefficient code, such as pointer arithmetic on non-pointer types or uninitialized memory accesses.
• Force unnecessary recompilations: Change code in ways that require recompilation, such as adding comments or modifying macros, to invalidate cached code paths and reduce the effectiveness of compiler optimizations.

Conclusion:

By incorporating these inefficiencies into the program, it is possible to significantly slow down its execution speed and highlight the importance of optimizing code for modern pipeline architectures. However, it is important to note that these techniques are not intended for practical use in real-world applications and are only meant to illustrate the potential impacts of poor optimization on performance.

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