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https://issues.apache.org/jira/browse/PARQUET-2159?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17695594#comment-17695594
]
ASF GitHub Bot commented on PARQUET-2159:
-----------------------------------------
jiangjiguang commented on code in PR #1011:
URL: https://github.com/apache/parquet-mr/pull/1011#discussion_r1122821855
##########
parquet-plugins/parquet-encoding-vector/src/test/java/org/apache/parquet/column/values/bitpacking/TestByteBitPacking512VectorLE.java:
##########
@@ -0,0 +1,172 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+package org.apache.parquet.column.values.bitpacking;
+
+import org.junit.Test;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.math.BigDecimal;
+import java.nio.ByteBuffer;
+import java.util.ArrayList;
+import java.util.List;
+
+import static org.junit.Assert.assertArrayEquals;
+
+public class TestByteBitPacking512VectorLE {
+ private static final Logger LOG =
LoggerFactory.getLogger(TestByteBitPacking512VectorLE.class);
+ @Test
+ public void unpackValuesUsingVector() {
+ if (ParquetReadRouter.getSupportVectorFromCPUFlags() !=
VectorSupport.VECTOR_512) {
+ LOG.info("avx512vbmi and avx512_vbmi2 are not supported, skip this
test.");
+ return;
+ }
Review Comment:
@gszadovszky I agree with you.
1、I have verified with `lscpu` command that there are not avx512vbmi and
avx512_vbmi2 instruction set on actions runner.
2、I have checked the
docs(https://docs.github.com/en/actions/using-github-hosted-runners/about-github-hosted-runners),
github actions do not support to select runner with specific instruction set.
3、I have resubmitted the help on how to select specific
runner(https://github.com/orgs/community/discussions/48955)
* I think there are two ways to fix it:
* a. skip vector related tests until github actions support to select
specific runner.
* b. I will add a **self-hosted runner** for parquet-mr repo with cloud
instance to run vector related tests. But the runner is not long time since I
have to pay money for it.
What do you think ? @gszadovszky
> Parquet bit-packing de/encode optimization
> ------------------------------------------
>
> Key: PARQUET-2159
> URL: https://issues.apache.org/jira/browse/PARQUET-2159
> Project: Parquet
> Issue Type: Improvement
> Components: parquet-mr
> Affects Versions: 1.13.0
> Reporter: Fang-Xie
> Assignee: Fang-Xie
> Priority: Major
> Fix For: 1.13.0
>
> Attachments: image-2022-06-15-22-56-08-396.png,
> image-2022-06-15-22-57-15-964.png, image-2022-06-15-22-58-01-442.png,
> image-2022-06-15-22-58-40-704.png
>
>
> Current Spark use Parquet-mr as parquet reader/writer library, but the
> built-in bit-packing en/decode is not efficient enough.
> Our optimization for Parquet bit-packing en/decode with jdk.incubator.vector
> in Open JDK18 brings prominent performance improvement.
> Due to Vector API is added to OpenJDK since 16, So this optimization request
> JDK16 or higher.
> *Below are our test results*
> Functional test is based on open-source parquet-mr Bit-pack decoding
> function: *_public final void unpack8Values(final byte[] in, final int inPos,
> final int[] out, final int outPos)_* __
> compared with our implementation with vector API *_public final void
> unpack8Values_vec(final byte[] in, final int inPos, final int[] out, final
> int outPos)_*
> We tested 10 pairs (open source parquet bit unpacking vs ours optimized
> vectorized SIMD implementation) decode function with bit
> width=\{1,2,3,4,5,6,7,8,9,10}, below are test results:
> !image-2022-06-15-22-56-08-396.png|width=437,height=223!
> We integrated our bit-packing decode implementation into parquet-mr, tested
> the parquet batch reader ability from Spark VectorizedParquetRecordReader
> which get parquet column data by the batch way. We construct parquet file
> with different row count and column count, the column data type is Int32, the
> maximum int value is 127 which satisfies bit pack encode with bit width=7,
> the count of the row is from 10k to 100 million and the count of the column
> is from 1 to 4.
> !image-2022-06-15-22-57-15-964.png|width=453,height=229!
> !image-2022-06-15-22-58-01-442.png|width=439,height=217!
> !image-2022-06-15-22-58-40-704.png|width=415,height=208!
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