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going-parallel-and-out-of-core-with-task-scheduli.json
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going-parallel-and-out-of-core-with-task-scheduli.json
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{
"alias": "video/3819/going-parallel-and-out-of-core-with-task-scheduli",
"category": "PyGotham 2015",
"copyright_text": "CC BY-SA",
"description": "Dask is an open source, pure python library that enables parallel\nlarger-than-memory computation in a novel way. We represent programs as\nDirected Acyclic Graphs (DAG) of function calls. These graphs are\nexecuted by dask's schedulers with different optimizations (synchronous,\nthreaded, parallel, distributed-memory). Dask has modules geared towards\ndata analysis, which provide a friendly interface to building graps. One\nmodule, dask.array, mimics a subset of NumPy operations. With dask.array\nwe can work with NumPy like arrays that are larger than RAM and\nparallelization comes for free by leveraging the underlying DAG.\n",
"duration": 2553,
"id": 3819,
"language": "eng",
"quality_notes": "",
"recorded": "2015-08-16",
"slug": "going-parallel-and-out-of-core-with-task-scheduli",
"speakers": [
"Blake Griffith"
],
"summary": "",
"tags": [],
"thumbnail_url": "https://archive.org/services/img/pyvideo_3819___Going_parallel_and_outofcore_with_task_scheduling",
"title": "Going parallel and larger-than-memory with graphs",
"videos": [
{
"type": "archive.org",
"url": "https://archive.org/details/pyvideo_3819___Going_parallel_and_outofcore_with_task_scheduling"
},
{
"length": 0,
"type": "youtube",
"url": "http://youtu.be/yDlCNjtZvLw"
}
]
}