Multi-GPU Scheduling With Uniform Latency and Thermal Awareness
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Solution Overview
Problem
Current graphics processing units (GPUs) face challenges in achieving deterministic scheduling across multiple GPUs, leading to inconsistent latency and inefficient workload distribution, particularly in thermally aware and thermally managed environments.
Innovation Solution
The implementation of a multi-GPU architecture with uniform latency, thermally aware workload scheduling, and end-to-end contract negotiation for workload distribution, utilizing advanced scheduling algorithms and memory management to optimize performance across multiple GPUs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional scheduling algorithms are used across multiple GPUs, then device complexity is reduced, but latency becomes inconsistent and deterministic scheduling cannot be achieved
Solution Approach 1:
The scheduling system is segmented into multiple independent components: a contract negotiation module that establishes service level agreements between GPUs, a workload distribution module that partitions tasks, and a latency compensation module that adjusts timing. This segmentation allows deterministic scheduling to be achieved through coordinated simple operations rather than a single complex scheduling algorithm.
Solution Approach 2:
An intermediary contract negotiation protocol is introduced between GPUs to establish service level agreements before workload execution. This intermediary layer abstracts the complexity of deterministic scheduling by translating high-level latency requirements into concrete workload distribution parameters, enabling reliable timing without requiring complex real-time scheduling decisions at each GPU.
2Productivity
If workload is distributed across multiple GPUs to improve productivity, then processing capacity increases, but thermal management becomes more difficult and latency consistency deteriorates
Solution Approach 1:
The system applies local quality by allowing each GPU to operate with customized workload characteristics tailored to its thermal state and performance capabilities. The contract negotiation protocol enables each GPU to declare its thermal constraints and receive appropriate workload adjustments, ensuring that high-productivity workloads are distributed to GPUs that can handle them thermally while maintaining deterministic latency through local adaptation rather than uniform workload distribution.
3Speed
If parallel processing is maximized in the graphics pipeline, then processing speed increases, but latency becomes unpredictable and deterministic timing cannot be guaranteed
Solution Approach 1:
The contract negotiation protocol performs preliminary actions by establishing service level agreements and workload distribution parameters before actual processing begins. This advance planning allows the system to reserve processing capacity and allocate resources in advance, ensuring that parallel processing operations can proceed at maximum speed while maintaining predictable latency bounds through pre-negotiated service level agreements rather than reactive scheduling.
4Reliability
If end-to-end contract negotiation is implemented for workload distribution, then latency consistency improves, but device complexity and communication overhead increase
Solution Approach 1:
The contract negotiation protocol implements partial action by negotiating only the essential parameters needed for deterministic latency (workload size, timing requirements, resource allocation) rather than all possible system parameters. This selective negotiation approach achieves latency consistency through agreement on critical timing and resource parameters while avoiding the communication overhead of comprehensive system-wide coordination.
Data Source
AI summary
Embodiments described herein include software, firmware, and hardware that provides techniques to enable deterministic scheduling across multiple general-purpose graphics processing units. One embodiment provides a multi-GPU architecture with uniform latency. One embodiment provides techniques to distribute memory output based on memory chip thermals. One embodiment provides techniques to enable thermally aware workload scheduling. One embodiment provides techniques to enable end to end contracts for workload scheduling on multiple GPUs.