Sparsity or Pruning refers to the technique of removing (pruning) weights during the model training to achieve smaller models. Such models are called sparse or pruned models. The simplest form of pruning is to [Image](/uploads/documents/4/7/8/6/478644f9764bfce5206bad33b4780f44/p2_1.jpg) Figure 5-1: An illustration of pruning weights (connections) and neurons (nodes) in a neural network consisting of fully connected layers Magnitude-Based Pruning. However, is this a good strategy to choose prunable weights? Are there other strategies that we can try? Let's introduce the concept of saliency scores to abstract the pruning strategies

s whether to enable dynamic pruning mode for partitioned tables. This feature is experimental. The default value of this variable is static, which means the dynamic pruning mode for partitioned tables when using UNION ALL to join a large number of SELECT statements #21441 – Optimize the dynamic pruning mode of partitioned tables to improve performance and stability #24150 – Fix the Region is Unavailable building the plan for queries that contain the HAVING clause #24045 Fix the issue that the column pruning improvement causes the Apply and Join operators' results to go wrong #23887 – Fix a bug that

issue that plan cache cannot detect changes of unsigned flags #28254 – Fix the wrong partition pruning when the partition function is out of range #28233 – Fix the issue that planner might cache invalid fails if NO_UNSIGNED_SUBTRACTION is set #26765 – Avoid expressions with side effects in column pruning and aggregation pushdown #27106 – Remove useless gRPC logs #24190 – Limit the valid decimal length rimental|N| |EXCHANGE PARTITION|Experimental|Experimental|Experimental|Experimental|N| |Dynamic Pruning|Experimental|Experimental|Experimental|N|N| #### 2.3.8 Statistics |Statistics|5.3|5.2|5.1|5.0|4

to wrong estimation. User document, #26085 ## • Improve index selection of the optimizer Add pruning rules for index selection. Before using the statistics for comparison, TiDB uses these rules to narrow rimental|Experimental|N| |EXCHANGE PARTITION|Experimental|Experimental|Experimental|N| |Dynamic Pruning|Experimental|Experimental|N|N| #### 2.3.8 Statistics |Statistics|5.2|5.1|5.0|4.0| |---|---|---|---|---| • List Partition (Introduced in v5.0) • List COLUMNS Partition (Introduced in v5.0) • Dynamic Pruning Mode for Partitioned Tables. (Introduced in v5.1) - The expression index feature. The expression

car mustang; drive(&mustang); // normal function call prune((tree*)&oak); // pruning a const tree }  car mustang; drive(&mustang); // normal function call prune((tree*)&oak); // pruning a const tree drive((car*)&oak); // driving a tree } Driving 1982 Driving 0 &oak); // pruning a const tree drive((car*)&oak); // driving a tree prune((tree*)&mustang); // pruning a car } Driving 1982 Driving 0 ![Image](/upl

2. Declarative Partitioning … 98 5.11.3. Partitioning Using Inheritance … 103 5.11.4. Partition Pruning … 108 5.11.5. Partitioning and Constraint Exclusion … 110 5.11.6. Best Practices for Declarative individual partition tables. CREATE INDEX ON measurement (logdate); Ensure that the enable_partition_pruning configuration parameter is not disabled in postgresql.conf. If it is, queries will not be optimized 11.4. Partition Pruning Partition pruning is a query optimization technique that improves performance for declaratively partitioned tables. As an example: SET enable_partition_pruning = on; – the default

2. Declarative Partitioning … 97 5.12.3. Partitioning Using Inheritance … 102 5.12.4. Partition Pruning … 107 5.12.5. Partitioning and Constraint Exclusion … 108 5.12.6. Best Practices for Declarative individual partition tables. CREATE INDEX ON measurement (logdate) ; Ensure that the enable_partition_pruning configuration parameter is not disabled in post- gresql.conf. If it is, queries will not be optimized 12.4. Partition Pruning Partition pruning is a query optimization technique that improves performance for declaratively partitioned tables. As an example: SET enable_partition_pruning = on; – the default

Disaster Recovery (DR) ··· 6723 17.4.6 Distributed eXecution Framework (DXF) ··· 6723 17.4.7 Dynamic Pruning ··· 6723 17.5 G ··· 6724 17.5.1 Garbage Collection (GC) ··· 6724 17.5.2 General Availability (GA) use dynamic pruning mode by default. Compared with static pruning mode, dynamic pruning mode supports features such as IndexJoin and plan cache with better performance. Therefore, static pruning mode will PARTITION|Y|Y|Y|Y|Y|Y|Y|N| |REORGANIZE PARTITION|Y|Y|Y|Y|Y|Y|N|N| |COALESCE PARTITION|Y|Y|Y|Y|Y|Y|N|N| |Dynamic pruning|Y|Y|Y|Y|Y|Y|Y|Y| |Range COLUMNS partitioning|Y|Y|Y|Y|Y|Y|Y|N| |Range INTERVAL partitioning|Y|Y|Y|Y|Y|Y|E|N|

Coalesce Partition ··· 6701 17.3.3 Continuous Profiling ··· 6701 17.4 D ··· 6701 17.4.1 Dynamic Pruning ··· 6701 17.5 I ··· 6701 17.5.1 Index Merge ··· 6701 17.5.2 In-Memory Pessimistic Lock ··· 6701 use dynamic pruning mode by default. Compared with static pruning mode, dynamic pruning mode supports features such as IndexJoin and plan cache with better performance. Therefore, static pruning mode will PARTITION|Y|Y|Y|Y|Y|Y|Y|N| |REORGANIZE PARTITION|Y|Y|Y|Y|Y|Y|N|N| |COALESCE PARTITION|Y|Y|Y|Y|Y|Y|N|N| |Dynamic pruning|Y|Y|Y|Y|Y|Y|Y|Y| |Range COLUMNS partitioning|Y|Y|Y|Y|Y|Y|Y|N| |Range INTERVAL partitioning|Y|Y|Y|Y|Y|Y|E|N|

Coalesce Partition ··· 6602 17.3.3 Continuous Profiling ··· 6602 17.4 D ··· 6602 17.4.1 Dynamic Pruning ··· 6602 17.5 I ··· 6602 17.5.1 Index Merge ··· 6602 17.5.2 In-Memory Pessimistic Lock ··· 6602 TiDB_INDEXES` - `INFORMATION_SCHEMA.TIDB_INDEX_USAGE` - `INFORMATION_SCHEMA.VIEWS` - Support partition pruning when partition expressions use the EXTRACT(YEAR_MONTH $ \rightarrow $ ...) function to improve query use the EXTRACT(YEAR_MONTH...) function, partition pruning is not supported, resulting in poor query performance. Starting from v8.3.0, partition pruning is supported when partition expressions use the EXTRACT(YEAR_MONTH