Task Graph Scheduling via Device Telemetry
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Solution Overview
Problem
Current data parallel programming models face inefficiencies in task graph scheduling due to sub-optimal heuristics and energy consumption modeling, particularly in heterogeneous architectures with diverse workloads, leading to limited code reusability and increased power consumption.
Innovation Solution
Implementing data parallel programming task graph optimization through device telemetry, which collects metrics like power consumption and cache access efficiencies to inform scheduling decisions, and dynamically adjusts optimization objectives based on executed task graph data for improved power and memory efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sub-optimal heuristics are used for task graph scheduling, then scheduling speed is improved, but power consumption increases and scheduling efficiency deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where device telemetry data from executed tasks is collected and used to refine future scheduling decisions. The system learns from actual execution outcomes to optimize power consumption and scheduling efficiency over time, resolving the contradiction between fast scheduling and energy efficiency.
Solution Approach 2:
The system dynamically changes scheduling parameters based on device telemetry and workload characteristics. By adapting parameters such as task priority, resource allocation, and execution timing based on real-time performance data, the system achieves both efficient scheduling and reduced power consumption without relying on fixed sub-optimal heuristics.
2Adaptability or versatility
If fixed scheduling heuristics are used, then implementation complexity is reduced, but adaptability to diverse workloads deteriorates
Solution Approach 1:
The scheduling system transitions from static fixed heuristics to dynamic adaptive scheduling that adjusts to diverse workloads. The system incorporates telemetry data collection, performance modeling, and adaptive decision-making capabilities that allow it to handle varying workload patterns effectively while managing complexity through structured learning frameworks.
Solution Approach 2:
The system performs preliminary actions by collecting and analyzing device telemetry data before making scheduling decisions. This pre-processing of performance information allows the scheduler to prepare optimized schedules in advance, improving adaptability to diverse workloads while maintaining manageable complexity through automated data preparation.
3Productivity
If telemetry data collection is implemented, then scheduling optimization is improved, but data processing overhead increases
Solution Approach 1:
The system extracts only the essential telemetry data needed for scheduling optimization rather than collecting all possible device metrics. By selectively extracting relevant performance parameters and filtering out unnecessary data, the system reduces processing overhead while maintaining scheduling optimization benefits.
Solution Approach 2:
The implementation collects and processes only the partial telemetry data necessary for effective scheduling decisions rather than comprehensive data collection. This partial action approach balances optimization needs with processing time constraints, avoiding excessive data collection while maintaining sufficient information for improved scheduling.
Data Source
AI summary
An apparatus to facilitate data parallel programming task graph optimization through device telemetry is disclosed. The apparatus includes a processor to: receive, from a compiler, compiled code generated from source code of an application, the compiled code to support a workload of the application; generate a task graph of the application using the compiled code, the task graph to represent at least one of a relationship or dependency of the compiled code; receive runtime telemetry data corresponding to execution of the compiled code on the one or more accelerator devices; identify one or more scheduling optimizations for the one or more accelerator devices based on the task graph and the received telemetry data; and provide a scheduling command to cause the one or more scheduling optimizations to be implemented in the one or more accelerator devices.