Extensible Data Parallel Semantics for Compute Node Adaptation
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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 programming data parallel compute nodes is complex due to semantic restrictions in existing data parallel languages.
Innovation Solution
A high-level programming language with extensible data parallel semantics allows user code to specify hardware and software resources using compute device and resource view objects, enabling execution on new and updated compute nodes and runtime libraries, thereby easing semantic restrictions and improving efficiency.
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 due to semantic restrictions in data parallel languages
Solution Approach 1:
The patent introduces a runtime library as an intermediary between the user code and the compute device. This runtime library handles the semantic restrictions and hardware-specific optimizations, allowing users to write portable code while the runtime manages the complexity of different compute device architectures. The runtime library acts as a mediator that translates high-level user code into device-specific instructions.
Solution Approach 2:
The patent creates a universal programming model that works across multiple types of compute devices (GPUs, FPGAs, CPUs) through a single high-level language interface. The extensible data parallel semantics enable the same user code to be executed on different hardware architectures without requiring language-specific modifications, achieving multi-functionality and portability.
2Ease of operation
If general purpose processors are used to execute data parallel algorithms, then programming ease is maintained, but processing efficiency deteriorates due to lack of optimized hardware resources
Solution Approach 1:
The patent creates a virtualized copy of the compute device through the runtime library and compute device object. This virtual representation allows the system to simulate or emulate data parallel execution on general purpose processors while maintaining the abstraction of specialized hardware. The virtualized compute device model enables software-based optimization without requiring physical specialized hardware.
Solution Approach 2:
The patent enables dynamic configuration of execution parameters through the compute device object and resource view object. Users can specify different execution models, precision levels, and hardware resources dynamically, allowing the system to adapt the execution strategy based on the target platform while maintaining high-level programming simplicity.
3Productivity
If code is designed for specific compute nodes, then performance optimization is achieved, but adaptability to new hardware deteriorates due to semantic restrictions
Solution Approach 1:
The patent introduces dynamic adaptability through the extensible data parallel semantics and runtime library. The system can dynamically adjust execution strategies, resource allocation, and optimization levels based on the target compute device. New hardware types can be added to the runtime library without requiring changes to user code, enabling continuous adaptation to emerging technologies.
Solution Approach 2:
The patent segments the execution system into distinct components: user code, runtime library, compute device object, and resource view object. This segmentation allows each component to be independently optimized and updated. The runtime library can be enhanced to support new hardware architectures while user code remains unchanged, maintaining both performance optimization and hardware adaptability.
Data Source
AI summary
A high level programming language provides extensible data parallel semantics. User code specifies hardware and software resources for executing data parallel code using a compute device object and a resource view object. The user code uses the objects and semantic metadata to allow execution by new and/or updated types of compute nodes and new and/or updated types of runtime libraries. The extensible data parallel semantics allow the user code to be executed by the new and/or updated types of compute nodes and runtime libraries.