Auto-Parallelizing Compiler for Event-Driven Tuple-Space Programs
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Solution Overview
Problem
Existing techniques for parallelism in computing, such as traditional approaches and polyhedral model-based systems, fail to systematically analyze and optimize event-driven tasks (EDTs) for efficient execution on multi-core architectures, leading to suboptimal performance and power consumption due to inadequate dependency analysis and runtime exploration of parallelism.
Innovation Solution
An auto-parallelizing compiler technology that generates event-driven, tuple-space-based programs by employing a hierarchical mapping strategy and a retargetable runtime API, allowing for systematic analysis and transformation of sequential programs into EDT instances, enabling fine-grained parallelism and adaptive scheduling across different EDT-based runtimes like CnC, SWARM, and OCR.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional parallelism approaches or polyhedral model-based systems are used, then programming models are simpler, but systematic analysis and optimization of event-driven tasks for efficient execution on multi-core architectures is insufficient
Solution Approach 1:
The patent introduces an intermediary dependency analysis module that systematically extracts and analyzes dependencies between event-driven tasks. This module acts as a mediator between the program code and the runtime scheduler, providing structured dependency information that enables efficient parallel execution while managing complexity through automated analysis.
Solution Approach 2:
The patent employs parameter changes by transforming the dependency representation into a standardized format that can be processed by the runtime system. By changing the representation parameters of dependencies into a manageable structure, the system achieves systematic analysis without overwhelming complexity in the programming model.
2Speed
If more parallelism is uncovered to hide communication latencies, then performance is improved, but dynamic balancing of parallelism becomes more difficult
Solution Approach 1:
The patent implements feedback mechanisms where the runtime system continuously monitors execution progress and adjusts task scheduling based on actual performance data. This feedback loop enables the system to dynamically balance parallelism by learning from execution patterns and adapting the degree of parallelism to current workload conditions, thereby hiding communication latencies while managing complexity through automated adaptation.
Solution Approach 2:
The patent applies dynamics by making the parallelism degree adjustable and adaptive rather than fixed. The runtime system dynamically modifies the number and distribution of parallel tasks based on real-time conditions, allowing the system to optimize performance by uncovering more parallelism when needed while avoiding excessive complexity through automated control.
3Ease of operation
If user specifies parallelism without systematic analysis, then programming is easier, but parallelization may not meet performance and power budget requirements
Solution Approach 1:
The patent implements self-service by enabling the system to automatically analyze dependencies and determine optimal parallelization strategies without requiring user intervention. The runtime system performs systematic analysis of the program code, extracts dependencies, and schedules tasks autonomously, thereby maintaining ease of programming while achieving performance and power efficiency through automated optimization.
Solution Approach 2:
The patent uses parameter changes by automatically adjusting parallelism parameters based on systematic dependency analysis. Instead of relying on user-specified parallelism, the system changes the degree and distribution of parallel tasks based on analyzed dependencies, ensuring that performance and power requirements are met while keeping the programming interface simple.
4Adaptability or versatility
If fine-grained parallelism is generated, then adaptive scheduling is enabled, but runtime overhead increases
Solution Approach 1:
The patent applies segmentation by dividing the program into fine-grained event-driven tasks that can be independently scheduled. This segmentation enables adaptive scheduling by creating small, manageable units that the runtime can dynamically assign and execute, improving adaptability while managing runtime overhead through efficient task granularity and prioritization.
Data Source
AI summary
In a system for automatic generation of event-driven, tuple-space based programs from a sequential specification, a hierarchical mapping solution can target different runtimes relying on event-driven tasks (EDTs). The solution uses loop types to encode short, transitive relations among EDTs that can be evaluated efficiently at runtime. Specifically, permutable loops translate immediately into conservative point-to-point synchronizations of distance one. A runtime-agnostic which can be used to target the transformed code to different runtimes.