{
  "id": "1707.04566",
  "version": "v1",
  "published": "2017-07-14T17:28:28.000Z",
  "updated": "2017-07-14T17:28:28.000Z",
  "title": "Pushing the Limits of Online Auto-tuning: Machine Code Optimization in Short-Running Kernels",
  "authors": [
    "Fernando Endo",
    "Damien Couroussé",
    "Henri-Pierre Charles"
  ],
  "comment": "Extension of a Conference Paper published in the proceedings of MCSoC-16: IEEE 10th International Symposium on Embedded Multicore/Many-core Systems-on-Chip, Lyon, France, 2016",
  "categories": [
    "cs.PF"
  ],
  "abstract": "We propose an online auto-tuning approach for computing kernels. Differently from existing online auto-tuners, which regenerate code with long compilation chains from the source to the binary code, our approach consists on deploying auto-tuning directly at the level of machine code generation. This allows auto-tuning to pay off in very short-running applications. As a proof of concept, our approach is demonstrated in two benchmarks, which execute during hundreds of milliseconds to a few seconds only. In a CPU-bound kernel, the average speedups achieved are 1.10 to 1.58 depending on the target micro-architecture, up to 2.53 in the most favourable conditions (all run-time overheads included). In a memory-bound kernel, less favourable to our runtime auto-tuning optimizations, the average speedups are 1.04 to 1.10, up to 1.30 in the best configuration. Despite the short execution times of our benchmarks, the overhead of our runtime auto-tuning is between 0.2 and 4.2% only of the total application execution times. By simulating the CPU-bound application in 11 different CPUs, we showed that, despite the clear hardware disadvantage of In-Order (io) cores vs. Out-of-Order (ooo) equivalent cores, online auto-tuning in io CPUs obtained an average speedup of 1.03 and an energy efficiency improvement of 39~\\% over the SIMD reference in ooo CPUs.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-07-14T17:28:28.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "machine code optimization",
      "online auto-tuning",
      "short-running kernels",
      "average speedup",
      "total application execution times"
    ],
    "tags": [
      "conference paper"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}