Mining JIT traces for missing optimizations with Z3
Using Z3, PyPy's JIT traces are analyzed to pinpoint inefficient integer operations for further optimization. By translating operations into Z3 formulas, redundancies are identified to enhance PyPy's JIT compiler efficiently.
Read original article
This post discusses using Z3 to identify missing optimizations in PyPy's JIT traces. By analyzing optimized traces of real benchmarks, Z3 can pinpoint inefficient integer operations that could have been optimized further. This method avoids the combinatorial explosion of trying all possible instruction sequences. The approach involves translating integer operations into Z3 formulas and identifying redundancies or constant results that could have been optimized. Examples include simplifying boolean operations and finding equivalent operations to save computation time. By synthesizing constants with Z3, potential optimizations can be uncovered, such as simplifying XOR operations or identifying relationships between variables. The post outlines a systematic process for mining JIT traces, detecting inefficiencies, and minimizing operations to enhance optimization. The ultimate goal is to improve PyPy's JIT compiler by addressing these missing optimizations.
Related
Spending too much time optimizing for loops
Researcher Octave Larose shared insights on optimizing Rust interpreters, focusing on improving performance for the SOM language. By enhancing loop handling and addressing challenges, significant speedups were achieved, balancing code elegance with efficiency.
Spending too much time optimizing for loops
Researcher Octave Larose discussed optimizing Rust interpreters, focusing on improving performance for the SOM language. They highlighted enhancing loop efficiency through bytecode and primitives, addressing challenges like Rust limitations and complex designs. Despite performance gains, trade-offs between efficiency and code elegance persist.
Do not taunt happy fun branch predictor
The author shares insights on optimizing AArch64 assembly code by reducing jumps in loops. Replacing ret with br x30 improved performance, leading to an 8.8x speed increase. Considerations on branch prediction and SIMD instructions are discussed.
Beating the Compiler
The blog post discusses optimizing interpreters in assembly to outperform compilers. By enhancing the Uxn CPU interpreter, a 10-20% speedup was achieved through efficient assembly implementations and techniques inspired by LuaJIT.
Finding Simple Rewrite Rules for the JIT with Z3
At PLDI conference, CF Bolz-Tereick presented a paper with Max Bernstein on using Z3 for superoptimization. They encoded PyPy's JIT integer operations into Z3 formulas to find and prove simplification rules efficiently.
4 commentsI ask because z3 has been used for type inference (Typpete) and for solving equations written in Python.
> (x & c1) | (x & c1) == x & (c1 | c2)
Is this a typo? Shoud the second c1 be c2 instead?
Related
Spending too much time optimizing for loops
Researcher Octave Larose shared insights on optimizing Rust interpreters, focusing on improving performance for the SOM language. By enhancing loop handling and addressing challenges, significant speedups were achieved, balancing code elegance with efficiency.
Spending too much time optimizing for loops
Researcher Octave Larose discussed optimizing Rust interpreters, focusing on improving performance for the SOM language. They highlighted enhancing loop efficiency through bytecode and primitives, addressing challenges like Rust limitations and complex designs. Despite performance gains, trade-offs between efficiency and code elegance persist.
Do not taunt happy fun branch predictor
The author shares insights on optimizing AArch64 assembly code by reducing jumps in loops. Replacing ret with br x30 improved performance, leading to an 8.8x speed increase. Considerations on branch prediction and SIMD instructions are discussed.
Beating the Compiler
The blog post discusses optimizing interpreters in assembly to outperform compilers. By enhancing the Uxn CPU interpreter, a 10-20% speedup was achieved through efficient assembly implementations and techniques inspired by LuaJIT.
Finding Simple Rewrite Rules for the JIT with Z3
At PLDI conference, CF Bolz-Tereick presented a paper with Max Bernstein on using Z3 for superoptimization. They encoded PyPy's JIT integer operations into Z3 formulas to find and prove simplification rules efficiently.