| commit | b1f5da432845a221b2ebb5abf11cc4521415c9f8 | [log] [tgz] |
|---|---|---|
| author | Leandro Lacerda <leandrolcampos@yahoo.com.br> | Wed Aug 27 23:05:10 2025 -0300 |
| committer | GitHub <noreply@github.com> | Wed Aug 27 21:05:10 2025 -0500 |
| tree | 102dde154ec4e6e547a28e157a8cb2c0db7d95f9 | |
| parent | 831a1540512a943f472e3d323084f6affeca5901 [diff] |
[libc][gpu] Add exp/log benchmarks and flexible input generation (#155727) This patch adds GPU benchmarks for the exp (`exp`, `expf`, `expf16`) and log (`log`, `logf`, `logf16`) families of math functions. Adding these benchmarks revealed a key limitation in the existing framework: the input generation mechanism was hardcoded to a single strategy that sampled numbers with a uniform distribution of their unbiased exponents. While this strategy is effective for values spanning multiple orders of magnitude, it is not suitable for linear ranges. The previous framework lacked the flexibility to support this. ### Summary of Changes **1. Framework Refactoring for Flexible Input Sampling:** The GPU benchmark framework was refactored to support multiple, pluggable input sampling strategies. * **`Random.h`:** A new header was created to house the `RandomGenerator` and the new distribution classes. * **Distribution Classes:** Two sampling strategies were implemented: * `UniformExponent`: Formalizes the previous logic of sampling numbers with a uniform distribution of their unbiased exponents. It can now also be configured to produce only positive values, which is essential for functions like `log`. * `UniformLinear`: A new strategy that samples numbers from a uniform distribution over a linear interval `[min, max)`. * **`MathPerf` Update:** The `MathPerf` class was updated with a generic `run_throughput` method that is templated on a distribution object. This makes the framework extensible to future sampling strategies. **2. New Benchmarks for `exp` and `log`:** Using the newly refactored framework, benchmarks were added for `exp`, `expf`, `expf16`, `log`, `logf`, and `logf16`. The test intervals were carefully chosen to measure the performance of distinct behavioral regions of each function.
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