Work Group Dispatching for Nested Parallelism Latency
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Solution Overview
Problem
Conventional processing techniques and devices, such as GPUs, are inefficient in handling load imbalance and nested parallelism at fine granularities, particularly in non-graphics applications, and impose limitations on the depth of parallelism nesting, leading to increased kernel dispatch latency.
Innovation Solution
Implementing a nested parallelism framework at the work-group level using virtualized work-group spawning through a work-group context stack, which enables efficient execution of both graphics and compute kernels by utilizing hardware load-balancing and adhering to hierarchical execution models of accelerated processors, allowing for fork-join parallelism and efficient resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional processing techniques are used, then processing can be performed, but processing efficiency is reduced due to increased latency and poor resource utilization
Solution Approach 1:
The patent segments the processing workflow into distinct work groups that can be independently managed and dispatched. Each work group represents a discrete unit of computation that can be processed separately, allowing the system to optimize dispatching and reduce latency by processing segments in parallel rather than as a monolithic task.
Solution Approach 2:
The system performs preliminary actions by pre-configuring work group contexts and preparing processing units in advance. Work groups are staged and ready for dispatch, and processing resources are pre-allocated based on predicted needs, reducing the latency associated with dynamic resource allocation during execution.
2Productivity
If conventional processing techniques are used, then processing can be performed, but resource utilization is poor due to load imbalance
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor the execution status and performance of work groups. This feedback information is used to dynamically adjust the distribution of subsequent work groups, balancing the load across processing units by directing more work to underutilized resources and less to overloaded ones, thereby improving overall resource utilization.
Solution Approach 2:
The system dynamically adapts work group dispatching based on real-time processing conditions. Processing units can change their state and capacity during execution, and the dispatching mechanism adjusts accordingly, allowing flexible resource allocation that responds to changing load conditions rather than following a static assignment pattern.
3Adaptability or versatility
If nested parallelism is implemented at kernel granularity, then parallel processing can be achieved, but the depth of parallelism nesting is limited and dispatch latency increases
Solution Approach 1:
The patent applies nested doll by implementing nested parallelism where work groups can contain and spawn child work groups, creating hierarchical nesting of computational tasks. This allows deep parallelism structures where parent work groups generate child work groups that can further spawn grandchildren, enabling complex nested algorithms to execute with minimal dispatch overhead since the nesting occurs within the work group execution model rather than at the kernel level.
Data Source
AI summary
A method and processing apparatus are provided for executing a program. The processing apparatus comprises memory and a processor. The processor is configured to dispatch a parent work group of a program to be executed and execute a spawn work group instruction to enable a child work group of the parent work group to be executed. The processor is also configured to dispatch the child work group for execution when a sufficient amount of resources are determined to be available to execute the child work group and execute the child work group on one or more compute units. The spawn work group instruction comprises a pointer to a synchronization variable, and the processor is also configured to execute a join workgroup instruction which comprises the pointer to the synchronization variable in the spawn work group instruction.