Multi-Threaded GPU Trace Replay via CPU-Based Emulation
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Solution Overview
Problem
Existing profiling and analysis tools for graphics processing units (GPUs) are limited due to the lack of operating system support and the complexity of parallel processing, making it difficult to efficiently analyze GPU workload and improve computational efficiency.
Innovation Solution
The generation and analysis of GPU long instruction traces (GLITs) are used to capture GPU states and operating parameters, allowing for efficient profiling and optimization by emulating GPU execution on a central processing unit (CPU).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If profiling instructions are inserted into GPU code to capture execution statistics, then measurement precision is improved, but device complexity increases due to the need to handle multi-threaded parallel execution and synchronization
Solution Approach 1:
The patent creates a simplified copy of the GPU execution environment that runs on the CPU. This copy includes replicated register files, instruction buffers, and execution logic that mirrors the original GPU's operational state. By copying the essential execution state rather than analyzing the complex multi-threaded GPU directly, the system achieves precise measurement of code execution statistics while avoiding the complexity of handling hundreds or thousands of parallel GPU threads.
Solution Approach 2:
The patent introduces an intermediary CPU-based emulator that acts as a mediator between the original GPU execution and the analysis tools. The emulator receives instruction traces from the GPU, reconstructs the execution state in a simplified manner, and presents this processed information to profiling tools. This intermediary layer translates the complex multi-threaded GPU execution model into a form that is easier to analyze while preserving the essential execution statistics.
2Productivity
If GPU code is profiled and analyzed directly on the GPU, then productivity is improved through faster analysis, but reliability decreases due to lack of operating system support and memory limitations
Solution Approach 1:
The patent copies the GPU execution state to a CPU-based environment where reliable profiling tools can operate. By creating a replicated execution model that runs on the CPU with full operating system support and access to expansive memory, the system maintains the productivity benefits of detailed profiling while achieving the reliability of a stable, well-supported computing environment.
Solution Approach 2:
The patent transitions the profiling and analysis process from the GPU dimension (where memory and OS support are limited) to the CPU dimension (where expansive memory and comprehensive OS support are available). This dimensional change allows profiling tools to operate with full reliability while still analyzing GPU code execution through the copied state, effectively resolving the contradiction between productivity and reliability.
3Power
If the number of parallel threads in GPU is increased to improve computational efficiency, then power increases, but difficulty of detecting and measuring code execution statistics worsens
Solution Approach 1:
The patent creates a simplified copy of the GPU execution model that runs on the CPU with a reduced number of threads. This copy reproduces the essential execution behavior and code execution statistics without requiring the full parallel threading capacity of the original GPU. By analyzing this simplified copy, the system can detect and measure code execution statistics effectively while avoiding the difficulty introduced by hundreds or thousands of simultaneous GPU threads.
Data Source
AI summary
At least one computer-readable storage medium comprising instructions for execution by at least one graphics processing unit (GPU) that, when executed, cause the at least one GPU to:obtain program code for tracing, the program code including a plurality of instructions;identify from the plurality of instructions of the program code events to be synchronized;instrument the program code corresponding to one or more of the events identified, by inserting instructions that support monitoring code;execute the instrumented program code on at least a plurality of hardware threads of the GPU and generate trace data;replay the identified events according to an order of occurrence of the events identified; andreport a GPU state indicating a utilization of the GPU based; andwherein to report the GPU state includes to indicate when the GPU executes non-graphics related tasks.