Co-map Communication Operator for Data Parallel Efficiency
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Solution Overview
Problem
General purpose processors are inefficient in executing data parallel algorithms due to the lack of optimized hardware resources, and existing programming tools do not adequately support the enhanced capabilities of data parallel compute nodes, making it difficult for programmers to effectively utilize these nodes.
Innovation Solution
A high-level programming language provides a co-map communication operator that maps an input indexable type to an output indexable type according to a function, allowing for efficient data parallel operations by transforming index spaces and enabling composability with other communication operators, thereby optimizing the use of data parallel compute nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data parallel compute nodes are used to execute data parallel algorithms, then processing efficiency is improved, but programming complexity increases
Solution Approach 1:
The patent introduces communication operators as intermediary abstractions that mediate between the programmer and the complex data parallel compute node architecture. These operators provide a high-level interface for data movement and transformation, shielding programmers from low-level hardware complexity while enabling efficient execution on data parallel architectures.
Solution Approach 2:
The communication operators are designed as universal constructs that can handle multiple types of data transformations and movements through a unified interface. The co-map operator, in particular, can lift functions to work on entire collections simultaneously, providing a multi-functional tool that simplifies various data parallel operations while maintaining programming simplicity.
2Ease of operation
If general purpose processors are used to execute data parallel algorithms, then programming ease is maintained, but processing efficiency deteriorates
Solution Approach 1:
Instead of requiring programmers to directly manage complex data parallel operations on hardware, the patent inverts the approach by providing high-level communication operators that automatically translate intuitive programming constructs into efficient data parallel operations. The co-map operator, for example, allows programmers to specify logical transformations without explicitly managing the underlying data parallel execution details.
3Adaptability or versatility
If communication operators are made composable through function lifting, then operator versatility is improved, but implementation complexity increases
Solution Approach 1:
The patent implements function lifting that allows communication operators to be nested and composed in a hierarchical manner. The co-map operator can lift functions to work on indexable types, which can themselves contain nested structures. This nested composition enables versatile data transformations while managing implementation complexity through structured, layered abstractions.
Data Source
AI summary
A high level programming language provides a co-map communication operator that maps an input indexable type to an output indexable type according to a function. The function maps an index space corresponding to the output indexable type to an index space corresponding to the input indexable type. By doing so, the co-map communication operator lifts a function on an index space to a function on an indexable type to allow composability with other communication operators.