Unified Ring Buffer Scheduling for GPU Compute Latency
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Solution Overview
Problem
Current computing systems face challenges in utilizing graphics processing units (GPUs) for general computation due to constrained programming environments, separate memory systems, inefficient scheduling, and limited support for non-graphics workloads, leading to high latency and power consumption issues when executing computational commands.
Innovation Solution
A method and system that allocate an accelerated processing device (APD) work queue via a kernel mode driver, selecting and dispatching commands through a compute process control block, enabling efficient execution of both graphics and computational tasks by treating the APD as a unified computing resource with its own memory and scheduling mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computational commands are dispatched to GPU for execution, then computational performance is improved, but latency increases due to high overhead of dispatching work
Solution Approach 1:
The patent segments the command dispatch mechanism by creating separate command queues for different types of workloads (graphics vs. compute). This allows compute commands to be processed through a dedicated pathway rather than competing with graphics commands, reducing dispatch overhead and latency while maintaining high computational throughput.
Solution Approach 2:
The patent introduces an intermediary command queue layer between the CPU and GPU compute units. This queue acts as a buffer and management layer that optimizes the dispatch of compute commands, reducing the overhead of direct CPU-GPU communication and enabling more efficient workload submission without increasing latency.
2Adaptability or versatility
If separate memory systems are used for CPU and GPU, then hardware independence is improved, but memory access efficiency deteriorates
Solution Approach 1:
The patent implements a unified memory address space that allows both CPU and GPU to access the same memory regions without requiring separate memory systems. This multi-functional memory architecture maintains hardware independence while enabling efficient direct memory access for both processors, eliminating the need for costly memory copies and improving overall memory access speed.
Solution Approach 2:
The patent merges the previously separate CPU and GPU memory address spaces into a single unified address space. This combination allows seamless memory sharing between processors, improving memory access efficiency while preserving the architectural independence of each processor through virtual memory management.
3Productivity
If GPU is used for general computation, then computational capabilities are improved, but programming complexity increases due to constrained programming environment
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically manages compute workload submission and GPU program selection. Instead of requiring developers to manually configure complex programming parameters, the system autonomously handles workload queuing, command dispatch, and program selection, significantly reducing programming complexity while maintaining enhanced computational capabilities.
Solution Approach 2:
The patent introduces an intermediary software layer that translates high-level compute requests into GPU-specific execution commands. This intermediary handles the complexity of GPU programming internally, allowing developers to use simplified interfaces while the system manages the underlying programming complexity, thus improving computational capabilities without increasing perceived programming complexity.
4Productivity
If computational workloads are offloaded to GPU, then processing throughput is improved, but power consumption increases
Solution Approach 1:
The patent implements dynamic workload management that adapts GPU utilization based on system state and workload characteristics. The system dynamically decides when to offload compute workloads to the GPU versus executing them on the CPU, optimizing the balance between processing throughput and power consumption by activating GPU acceleration only when it provides net energy efficiency benefits.
Solution Approach 2:
The patent changes the operational parameters of the GPU by implementing fine-grained control over compute command dispatch and execution. By adjusting parameters such as command queue depth, synchronization frequency, and workload batching, the system optimizes GPU power consumption while maintaining high processing throughput for computational workloads.
Data Source
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AI summary
A method, system, and computer program product are disclosed for providing improved access to accelerated processing device compute resources to user mode applications. The functionality disclosed allows user mode applications to provide commands to an accelerated processing device without the need for kernel mode transitions in order to access a unified ring buffer. Instead, applications are each provided with their own buffers, which the accelerated processing device hardware can access to process commands. With full operating system support, user mode applications are able to utilize the accelerated processing device in much the same way as a CPU.