{
  "annotation_id": "f2eebd7a-aeca-436d-82e9-0e207a45164b",
  "date_created": "2026-03-02T18:01:21.615000Z",
  "date_modified": "2026-03-02T18:01:21.615000Z",
  "file_hash": "56affe4fd3c416e792636cadcdcbb8f3a919b2f0b6acdf851cb26697d9a98c61",
  "private": false,
  "record": {
    "abstract": "Monte Carlo (MC) methods for numerical integration seem to be embarassingly\nparallel on first sight. When adaptive schemes are applied in order to enhance\nconvergence however, the seemingly most natural way of replicating the whole\njob on each processor can potentially ruin the adaptive behaviour. Using the\npopular VEGAS-Algorithm as an example an economic method of semi-micro\nparallelization with variable grain-size is presented and contrasted with\nanother straightforward approach of macro-parallelization. A portable\nimplementation of this semi-micro parallelization is used in the xloops-project\nand is made publicly available.",
    "arxiv_id": "physics/9710028",
    "authors": [
      "Richard Kreckel"
    ],
    "categories": [
      "physics.comp-ph"
    ],
    "doi": "10.1016/S0010-4655(97)00099-4",
    "journal_ref": "Comp. Phys. Comm. 106 (1997) 258-266",
    "title": "Parallelization of adaptive MC Integrators",
    "url": "https://arxiv.org/abs/physics/9710028"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "0b4667c9-1412-48d9-98eb-4860f9bb8e74",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}