Data Transform Accelerator Auto-Tuning for Throughput-Latency Balance
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Solution Overview
Problem
Existing data transform accelerators face challenges in optimizing performance metrics such as throughput, latency, and resource utilization due to varying workloads and system architectures, leading to inefficiencies in data processing operations.
Innovation Solution
A method and system for automatically tuning tunable parameters of a data transform accelerator by configuring a resource configuration vector based on performance metrics, adjusting parameters like container numbers, thread counts, and load balancing algorithms to achieve target performance thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data transform accelerator uses fixed resource configuration, then device complexity is reduced, but performance metrics cannot adapt to varying workloads leading to inefficiency
Solution Approach 1:
The patent implements dynamic resource configuration by allowing the data transform accelerator to automatically adjust its operational parameters (such as number of transform engines, buffer sizes, and pipeline stages) based on real-time workload characteristics. This enables the system to adapt its complexity to match the actual processing needs, improving efficiency without permanently increasing device complexity.
Solution Approach 2:
The system changes operational parameters of the data transform accelerator based on measured performance metrics and workload analysis. By adjusting parameters such as transformation type, buffer allocation, and engine configuration, the system optimizes data processing efficiency for different workload scenarios without requiring physical hardware changes.
2Productivity
If data transform accelerator increases resource allocation, then throughput is improved, but latency and resource utilization become unoptimized
Solution Approach 1:
The patent implements a feedback mechanism that continuously monitors performance metrics including throughput, latency, and resource utilization. Based on this feedback, the system automatically adjusts resource allocation and configuration parameters to maintain optimal performance balance, preventing both over-provisioning and under-provisioning of resources.
Solution Approach 2:
The system applies partial resource allocation based on actual workload requirements rather than allocating maximum resources continuously. By dynamically adjusting the degree of resource utilization, the system achieves adequate throughput while minimizing unnecessary latency and improving overall resource efficiency.
3Productivity
If data transform accelerator uses manual parameter tuning, then performance can be optimized, but ease of operation is reduced
Solution Approach 1:
The patent implements self-service automation where the data transform accelerator automatically performs parameter tuning and optimization without requiring manual intervention. The system uses embedded performance monitoring and decision-making algorithms to autonomously adjust configuration parameters, maintaining optimal performance while simplifying operation for the user.
Solution Approach 2:
The system introduces an intermediary automated tuning layer between the user and the underlying complex parameters. This intermediary automatically translates high-level performance goals into specific configuration adjustments, eliminating the need for users to manually tune complex technical parameters while still achieving optimized performance.
Data Source
AI summary
A method may include obtaining multiple tunable parameters associated with a data transform accelerator operable to perform data transform operations. The method may also include configuring a resource configuration vector based on the multiple tunable parameters. The method may further include obtaining a target performance metric. The method may also include measuring one or more performance metrics associated with the data transform accelerator. The method may further include automatically tuning at least one tunable parameter of the multiple tunable parameters to obtain tuned parameters in response to a performance metric of the one or more performance metrics failing to satisfy the target performance metric. The method may also include updating the resource configuration vector in view of the tuned parameters.