· · · · · · · · · · · · · · · · · · · · · · · · · · 5240 14.17.4 PD schedules based on topology label· · · · · · · · · · · · · · · · · · · · · · · · · · · · · 5241 14.18 URI Formats of External Storage indexes">Global indexes for partitioned tables (GA) Global indexes can effectively improve the efficiency of retrieving non- �→ partitioned columns, and remove the restriction that a unique key �→ must scanning tasks based on node scale and hardware specifications. This �→ improves statistics collection efficiency by fully utilizing system �→ resources, reduces manual tuning, and ensures stable cluster �→ performance

· · · · · · · · · · · · · · · · · · · · · · · · · · 5219 14.17.4 PD schedules based on topology label· · · · · · · · · · · · · · · · · · · · · · · · · · · · · 5220 14.18 URI Formats of External Storage indexes">Global indexes for partitioned tables (GA) Global indexes can effectively improve the efficiency of retrieving non- �→ partitioned columns, and remove the restriction that a unique key �→ must scanning tasks based on node scale and hardware specifications. This �→ improves statistics collection efficiency by fully utilizing system �→ resources, reduces manual tuning, and ensures stable cluster �→ performance

· · · · · · · · · · · · · · · · · · · · · · · · · · 5167 14.17.4 PD schedules based on topology label· · · · · · · · · · · · · · · · · · · · · · · · · · · · · 5168 21 14.18 URI Formats of External Storage partitioned tables (experimental) Global indexes can effectively improve the efficiency of retrieving non- �→ partitioned columns, and remove the restriction that a unique key �→ must mance for high NDV data #9196 @guo-shaoge Before v8.3.0, TiFlash has low aggregation calculation efficiency during the first stage of HashAgg aggregation when handling data with high NDV (number of distinct

· · · · · · · · · · · · · · · · · · · · · · · · · · 5138 14.17.4 PD schedules based on topology label· · · · · · · · · · · · · · · · · · · · · · · · · · · · · 5139 21 14.18 URI Formats of External Storage loading �→ efficiency by up to 10 times For clusters with a large number of tables and partitions, such as SaaS �→ or PaaS services, improvement in statistics loading efficiency can �→ solve release. For more information, see documentation. 2.2.1.2 Reliability • Improve statistics loading efficiency by up to 10 times #52831 @hawkingrei SaaS or PaaS applications can have a large number of data

· · · · · · · · · · · · · · · · · · · · · · · · · · 5092 14.17.4 PD schedules based on topology label· · · · · · · · · · · · · · · · · · · · · · · · · · · · · 5093 21 14.18 URI Formats of External Storage �→ Sort (GA in v8.0.0) The Global Sort feature aims to improve the stability and efficiency of �→ IMPORT INTO and CREATE INDEX. By globally �→ sorting the data s view to provide usage statistics of �→ indexes. This feature helps you assess the efficiency of indexes in �→ the database and optimize the index design. Data Migration

machine learning on embedded devices C++ is widely used in embedded systems: ◮ Was designed with efficiency in mind ◮ Provides low-level access to hardware resources ◮ Provides high-level abstractions 9 std::ifstream labels_ifs{labels_path}; 10 std::string label; 11 std::vector labels; 12 while (getline(labels_ifs, label)) { 13 labels.push_back(std::move(label)); 14 } 19 / 502. Build interpreter 1 // consumption with 4 threads: 93MB 31 / 50Comparision with Python Comparision made with TensorFlow Lite’s label image.py C++ Python Running duration (s) 1 7 Number of threads Inference duration (ms) C++ Python

because of a strong focus on performance from the inception of the project, Julia's computational efficiency exceedsCHAPTER 1. JULIA 1.11 DOCUMENTATION 4 that of other dynamic languages, and even rivals Advantages of Julia Julia aims to create an unprecedented combination of ease-of-use, power, and efficiency in a single lan- guage. In addition to the above, some advantages of Julia over comparable systems AnnotatedString wraps another string and allows for regions of it to be annotated with labelled values (:label => value). AllCHAPTER 8. STRINGS 71 generic string operations are applied to the underlying string

because of a strong focus on performance from the inception of the project, Julia's computational efficiency exceedsCHAPTER 1. JULIA 1.11 DOCUMENTATION 4 that of other dynamic languages, and even rivals Advantages of Julia Julia aims to create an unprecedented combination of ease-of-use, power, and efficiency in a single lan- guage. In addition to the above, some advantages of Julia over comparable systems AnnotatedString wraps another string and allows for regions of it to be annotated with labelled values (:label => value). AllCHAPTER 8. STRINGS 71 generic string operations are applied to the underlying string