Boost Performance with APL Workshop Insights
Explore key insights from the Performance Workshop Dyalog 22 by Aaron Hsu, covering topics on theory, factors affecting performance, implementation strategies, and more. Understand concepts like Big-O families, run-time memory usage, and data structures for enhancing computational efficiency.
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Presentation Transcript
Performance Workshop Dyalog 22 Aaron Hsu - aaron@dyalog.com
Theory Physical Implementation
The Comp. Sci. of APL Performance
Simplified Reasoning
Big-O Families
Constant: Log-Linear: Linear: Polynomial: O(n*k) Exponential: O(k*n) O(k) O(n n) O(n)
Theoretical Factors of Performance
Run time Memory Usage Memory Access Locality/Caching Parallelism/Critical Path Throughput/Bandwidth Overheads
Physical Factors of Performance
How many elements? How many bits? How many times? How many?
How much data do you have?
Implementation Factors for Performance
Platform Runtime Execution Model
The Array Data Structure
<Array Structure Diagram>
Code Data Structure
Token Stream
Variable Lookup
Auxiliary Structures
Caching/ Hashtables
Idiomatic APL and Automatic Performance Analysis
]RunTime ]SpaceNeeded
f0 {+} f1 {(1 ) 2- 0,(1 ,1) + } f2 {(1 ) (+ ){+ } } f3 {1 x x[+ ]+ x 0 1++ }
Memory Management
Lets look at some tricks
Vs. Hashed Primitives
Inverted Tables
When not to use inverted tables?
Flat/Nested Recursive ADTs
Calculate space of a tree
More Code to Play with
Brians Code
Basic Rules of Thumb
Flat Small Primitive Parallel
Where can APL outperform traditional languages? How?
