vertexclique

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vertexclique

PreprocessorDefinitions=
Techniques=Unsharp@Unsharp.fx,Colourfulness@Colourfulness.fx,Lightroom@qUINT_lightroom.fx,ContrastAdaptiveSharpen@CAS.fx

[CAS.fx]
Contrast=0.000000
Sharpening=1.000000

[Colourfulness.fx]
backbuffer_bits=8.000000
colourfulness=1.310000

vertexclique

Techniques=Unsharp@Unsharp.fx,Colourfulness@Colourfulness.fx,Lightroom@qUINT_lightroom.fx,ContrastAdaptiveSharpen@CAS.fx
TechniqueSorting=Unsharp@Unsharp.fx,Colourfulness@Colourfulness.fx,Lightroom@qUINT_lightroom.fx,ContrastAdaptiveSharpen@CAS.fx,AdaptiveSharpen@AdaptiveSharpen.fx,AdaptiveTonemapper@AdaptiveTonemapper.fx,ADOF@qUINT_dof.fx,AdvancedCRT@CRT.fx,AmbientLight@AmbientLight.fx,ArcaneBloom@ArcaneBloom.fx,ArtisticVignette@ArtisticVignette.fx,ASCII@ASCII.fx,AspectRatioSuite@AspectRatioSuite.fx,Before@Splitscreen.fx,After@Splitscreen.fx,Bloom@qUINT_bloom.fx,BloomAndLensFlares@Bloom.fx,Border@Border.fx,CA@ChromaticAberration.fx,Cartoon@Cartoon.fx,Checkerboard@Checkerboard.fx,ColorLab@ColorLab.fx,ColorMatrix@ColorMatrix.fx,CRT_Lottes@CRT_Lottes.fx,CRT_Yee64@CRT_Yee64.fx,CRT_Yeetron@CRT_Yeetron.fx,Cursor@Cursor.fx,Curves@Curves.fx,Daltonize@Daltonize.fx,Deband@Deband.fx,Debanding@qUINT_deband.fx,DELC_Sharpen@qUINT_sharp.fx,DepthAlpha@DepthAlpha.fx,DisplayDepth@DisplayDepth.fx,Dither@Dither.fx,DPX@DPX.fx,Fi

vertexclique

exact_buf_write/4.00 KiB
                        time:   [121.39 ns 122.07 ns 122.79 ns]
                        thrpt:  [31.067 GiB/s 31.251 GiB/s 31.424 GiB/s]
                 change:
                        time:   [-1.6055% -1.0408% -0.4293%] (p = 0.00 < 0.05)
                        thrpt:  [+0.4312% +1.0517% +1.6317%]
                        Change within noise threshold.
Found 4 outliers among 100 measurements (4.00%)
  4 (4.00%) high mild
exact_buf_write/256 KiB time:   [71.903 ns 72.184 ns 72.523 ns]

vertexclique

use lever::index::zonemap::ZoneMap;

fn main() {
  let customers: Vec<i32> = vec![
      vec![1, 0, -1, -2].repeat(2),
      vec![1, 2, 3, 4].repeat(3)
  ].concat();
  let products = vec![4, 3, 2, 1].repeat(100);
  let payouts = vec![4, 2, 6, 7].repeat(100);

vertexclique

@article{Ziauddin2017,
abstract = {In recent years, the data warehouse industry has witnessed decreased use of indexing but increased use of compression and clustering of data facilitating efficient data access and data pruning in the query processing area. A classic example of data pruning is the partition pruning, which is used when table data is range or list partitioned. But lately, techniques have been developed to prune data at a lower granularity than a table partition or sub-partition. A good example is the use of data pruning structure called zone map. A zone map prunes zones of data from a table on which it is defined. Data pruning via zone map is very effective when the table data is clustered by the filtering columns. The database industry has offered support to cluster data in tables by its local columns, and to define zone maps on clustering columns of such tables. This has helped improve the performance of queries that contain filter predicates on local columns. However, queries in data warehou

vertexclique

use lever::index::zonemap::ZoneMap;

fn main() {
  let customers: Vec<i32> = vec![
      vec![1, 0, -1, -2].repeat(500),
      vec![1, 2, 3, 4].repeat(250)
  ].concat();

  let ingestion_data = vec![
      ("customers", customers.as_slice()),

vertexclique

test result: ok. 0 passed; 0 failed; 501 ignored; 0 measured; 0 filtered out

     Running /home/vertexclique/projects/arrow/rust/target/release/deps/aggregate_kernels-6d55c08b7328dd4e
sum 512                 time:   [631.40 ns 631.72 ns 632.14 ns]                     
                        change: [+0.0700% +0.1267% +0.1817%] (p = 0.00 < 0.05)
                        Change within noise threshold.
Found 20 outliers among 100 measurements (20.00%)
  1 (1.00%) low mild
  19 (19.00%) high severe

vertexclique

=========== RwLock =============
Jun 07 12:06:40.728  INFO benchmark{mix=Mix { read: 94, insert: 2, remove: 1, update: 3, upsert: 0 } threads=1}: bustle: generating operation mix
Jun 07 12:06:40.729  INFO benchmark{mix=Mix { read: 94, insert: 2, remove: 1, update: 3, upsert: 0 } threads=1}: bustle: generating key space
Jun 07 12:06:40.912  INFO benchmark{mix=Mix { read: 94, insert: 2, remove: 1, update: 3, upsert: 0 } threads=1}: bustle: constructing initial table
Jun 07 12:06:40.941  INFO benchmark{mix=Mix { read: 94, insert: 2, remove: 1, update: 3, upsert: 0 } threads=1}: bustle: start workload mix
Jun 07 12:06:45.524  INFO benchmark{mix=Mix { read: 94, insert: 2, remove: 1, update: 3, upsert: 0 } threads=1}: bustle: workload mix finished took=4.582992212s ops=25165824 avg=181ns
25165824 operations across 1 thread(s) in 4.582992212s; time/op = 181ns
Jun 07 12:06:45.600  INFO benchmark{mix=Mix { read: 94, insert: 2, remove: 1, update: 3, upsert: 0 } threads=2}: bustle: generating operation mix
Jun 07 12:06:

vertexclique