AI Request Scheduler for NLP Workload Latency
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Solution Overview
Problem
Conventional hardware queue managers (HQM) face challenges in achieving optimal scheduling performance for natural language processing (NLP) workloads due to variable runtime conditions, leading to inefficiencies such as increased traffic, page table misses, cache misses, and latency.
Innovation Solution
A performance-enhanced computing system that includes a data movement accelerator and an AI request scheduler, which samples machine learning data to estimate complexity based on sparsity thresholds and telemetry data, allowing for dynamic selection of function implementations to optimize task scheduling on accelerators like FPGAs and GPUs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional hardware queue managers are used for scheduling NLP workloads, then basic scheduling functionality is provided, but optimal scheduling performance cannot be achieved due to variable runtime conditions
Solution Approach 1:
The system dynamically adapts scheduling decisions based on runtime conditions by monitoring data characteristics (sparsity, complexity) and adjusting scheduling parameters in real-time. The hardware queue manager transitions from static scheduling to dynamic scheduling that responds to varying workload characteristics, achieving optimal performance across different runtime scenarios.
Solution Approach 2:
The system implements feedback mechanisms by analyzing runtime data characteristics and using this information to adjust scheduling decisions. The queue manager receives feedback about data sparsity and complexity metrics, then modifies scheduling behavior accordingly, creating a closed-loop control system that optimizes performance based on actual conditions.
2Adaptability or versatility
If software-based scheduling methods are used, then flexibility in handling variable conditions is achieved, but increased latency and bandwidth usage occur
Solution Approach 1:
The system replaces software-based scheduling mechanisms with hardware-based scheduling logic implemented in the queue manager. This substitution of mechanical/software systems with hardware systems maintains adaptability to variable conditions while significantly reducing processing latency and bandwidth consumption, as hardware operations execute faster and with lower overhead.
3Productivity
If data is transferred between memory and accelerators without estimation, then simple data movement is achieved, but increased traffic and cache misses occur
Solution Approach 1:
The system performs preliminary estimation of data complexity and sparsity characteristics before initiating data transfer between memory and accelerators. By pre-assessing data properties, the system can make informed scheduling decisions about which data to transfer, when to transfer it, and to which accelerator, thereby reducing unnecessary data movement, minimizing cache misses, and lowering bandwidth consumption.
Data Source
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AI summary
Systems, apparatuses and methods may provide for technology that samples machine learning (ML) data from a local memory in accordance with a specified configuration, wherein the ML data is associated with one or more tasks submitted by one or more processor cores. The technology may also estimate the complexity of the sampled ML data based on one or more thresholds and schedule the task(s) for execution by one or more accelerators based on the complexity and telemetry data associated with a link to the accelerator(s).