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| (cudf-polars-bench) root@gpu-h100-0468:/app# for query in {1..22}; do | |
| > python -m cudf_polars.experimental.benchmarks.pdsh \ | |
| > --executor="streaming" \ | |
| > --runtime="rapidsmpf" \ | |
| > --path="/data/tpch-rs/scale-1000" \ | |
| > --suffix="" \ | |
| > --stream-policy="pool" \ | |
| > --n-workers 1 \ | |
| > --no-print-results \ | |
| > --no-summarize \ |
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| // ----------------------------------------------------------------------------- | |
| // Reproducer (translated from Python) | |
| // ----------------------------------------------------------------------------- | |
| #include <cudf/column/column.hpp> | |
| #include <cudf/column/column_factories.hpp> | |
| #include <cudf/strings/contains.hpp> | |
| #include <cudf/strings/regex/regex_program.hpp> | |
| #include <cudf/strings/strings_column_view.hpp> | |
| #include <cudf/types.hpp> |
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| // ----------------------------------------------------------------------------- | |
| // Reproducer (translated from Python) | |
| // ----------------------------------------------------------------------------- | |
| #include <cudf/column/column.hpp> | |
| #include <cudf/column/column_factories.hpp> | |
| #include <cudf/strings/contains.hpp> | |
| #include <cudf/strings/regex/regex_program.hpp> | |
| #include <cudf/strings/strings_column_view.hpp> | |
| #include <cudf/types.hpp> |
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| #!/usr/bin/env python3 | |
| """ | |
| Stream-ordering race using CuPy with RMM's async memory resource. | |
| This should trigger compute-sanitizer's stream-ordered race detection. | |
| """ | |
| import cupy as cp | |
| import rmm.mr | |
| from rmm.allocators.cupy import rmm_cupy_allocator |
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| #!/usr/bin/env python3 | |
| """ | |
| Convert cudf-polars traces from JSONL format to Chrome Tracing format. | |
| Chrome Tracing format can be viewed in chrome://tracing or similar tools. | |
| """ | |
| import json | |
| import sys | |
| from pathlib import Path |
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| """ | |
| Outputs the following (parital) IR for query 3. Note the multiple `Select` nodes. | |
| Sort( | |
| schema={'l_orderkey': <DataType(polars=Int64, plc=<type_id.INT64: 4>)>, 'revenue': <DataType(polars=Float64, plc=<type_id.FLOAT64: 10>)>, 'o_orderdate': <DataType(polars=Datetime(time_unit='ms', time_zone=None), plc=<type_id.TIMESTAMP_MILLISECONDS: 14>)>, 'o_shippriority': <DataType(polars=Int32, plc=<type_id.INT32: 3>)>}, | |
| by=(NamedExpr(revenue, Col(<DataType(polars=Float64, plc=<type_id.FLOAT64: 10>)>, 'revenue')), NamedExpr(o_orderdate, Col(<DataType(polars=Datetime(time_unit='ms', time_zone=None), plc=<type_id.TIMESTAMP_MILLISECONDS: 14>)>, 'o_orderdate'))), | |
| order=(<order.DESCENDING: 1>, <order.ASCENDING: 0>), | |
| null_order=(<null_order.AFTER: 0>, <null_order.BEFORE: 1>), | |
| stable=False, | |
| zlice=(0, 10), |
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| # /// script | |
| # requires-python = ">=3.13" | |
| # dependencies = [ | |
| # "dask-cuda", | |
| # "kvikio-cu12", | |
| # "nvidia-ml-py", | |
| # "psutil", | |
| # "rich", | |
| # ] | |
| # /// |
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| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <meta charset="utf-8"> | |
| </head> | |
| <body> | |
| <div id="app"></div> | |
| <script>var pyinstrumentHTMLRenderer=function(){"use strict";var is=Object.defineProperty;var ns=(F,ve,Pe)=>ve in F?is(F,ve,{enumerable:!0,configurable:!0,writable:!0,value:Pe}):F[ve]=Pe;var A=(F,ve,Pe)=>ns(F,typeof ve!="symbol"?ve+"":ve,Pe);function F(){}function ve(i){return i()}function Pe(){return Object.create(null)}function oe(i){i.forEach(ve)}function pt(i){return typeof i=="function"}function re(i,e){return i!=i?e==e:i!==e||i&&typeof i=="object"||typeof i=="function"}function ki(i){return Object.keys(i).length===0}function St(i,...e){if(i==null){for(const n of e)n(void 0);return F}const t=i.subscribe(...e);return t.unsubscribe?()=>t.unsubscribe():t}function ge(i,e,t){i.$$.on_destroy.push(St(e,t))}function Ci(i,e,t){return i.set(t),e}function u(i,e){i.appendChild(e)}function S(i,e,t){i.insertBefore(e,t||null)}function L |
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| Ran query=1 in 12.8907s | |
| Ran query=1 in 1.4123s | |
| Ran query=1 in 2.0518s | |
| Ran query=2 in 1.9878s | |
| Ran query=2 in 1.1087s | |
| Ran query=2 in 0.5492s | |
| Ran query=3 in 6.8526s | |
| Ran query=3 in 3.0100s | |
| Ran query=3 in 3.1382s | |
| Ran query=4 in 1.5490s |
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| """ | |
| Sample demonstrating slowness of cudf.read_parquet with kvikio from S3. | |
| # Instructions | |
| Download the dev wheels (skip if you have it built locally; rquires vpn access) | |
| curl -LO https://downloads.rapids.ai/ci/cudf/pull-request/17957/793d0b2/cudf_wheel_python_cudf_cu12_py312_x86_64.tar.gz | |
| curl -LO https://downloads.rapids.ai/ci/cudf/pull-request/17957/793d0b2/cudf_wheel_cpp_libcudf_cu12_x86_64.tar.gz |