Graph Compilation Concurrency Control for Latency and Resource Tradeoffs
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Solution Overview
Problem
Existing graph-based program compilation methods fail to optimize computational latency and efficiency effectively, as they do not adequately differentiate between components that can concurrently execute and those that cannot, leading to suboptimal performance in terms of latency and resource utilization.
Innovation Solution
The approach groups components into execution sets based on an optimality criterion that allows concurrency only where it results in improved computational efficiency and reduced latency, using a compiler/interpreter to identify and prepare execution sets for parallel execution on a computing platform with multiple nodes, enabling flexible data and control flow management through different types of ports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If components are allowed to execute concurrently to reduce computational latency, then execution time is reduced, but resource overhead and system complexity increase
Solution Approach 1:
The system segments components into execution sets based on their concurrency characteristics. High-latency operations are identified and placed in separate execution sets that can run concurrently, while low-latency operations are grouped together to execute sequentially. This segmentation allows the system to selectively apply concurrency only where it provides benefit, reducing unnecessary resource overhead while still achieving latency reduction for critical operations.
Solution Approach 2:
The compilation system dynamically determines the execution model for each component based on its latency characteristics. The system adapts its resource allocation and execution strategy according to the specific needs of each operation, allowing flexible transition between sequential and concurrent execution modes. This dynamic approach optimizes the balance between latency reduction and resource utilization for each component individually.
2Productivity
If all components are grouped into execution sets for parallel execution, then throughput increases, but execution efficiency decreases due to unnecessary parallelization overhead
Solution Approach 1:
The system applies different execution qualities to different components based on their local characteristics. High-latency components receive the quality of concurrent execution with full parallelization benefits, while low-latency components receive sequential execution to avoid parallelization overhead. This local differentiation ensures that each component executes with the most appropriate mode for its specific needs, optimizing overall system efficiency.
Solution Approach 2:
Instead of applying full parallelization to all components, the system applies partial parallelization only to the extent necessary. By identifying and isolating only the high-latency operations that benefit from concurrency, the system avoids the excessive action of parallelizing low-latency operations where it would provide no benefit and would only add overhead. This selective approach maintains high throughput for critical paths while preserving execution efficiency for non-critical operations.
Data Source
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AI summary
A received graph-based program specification (900, 1000) includes: a plurality of components (658, 660, 662), each corresponding to at least one operation; and a plurality of directed links each connecting an upstream component to a downstream component. Processed code is generated representing one or more groups of operations by: identifying a possible level of concurrency in a first group of operations based at least in part on a topology of the graph, such that multiple operations in the first group are not prevented by the topology of the graph from executing concurrently; analyzing at least some of the operations in the first group to determine runtime characteristics associated with the analyzed operations; and generating processed code for executing the operations, where the processed code enforces a reduced level of concurrency in the first group, lower than the identified possible level of concurrency, based at least in part on the determined runtime characteristics.