Hardware Call Stack Attribution for GPU Performance Tracking
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Solution Overview
Problem
Current methods for measuring application performance on processors, such as profilers and hardware-based techniques like Last Branch Record (LBR) and Branch Trace Store (BTS, are either invasive, cause performance changes, or provide inaccurate results due to limitations in storing performance-related information, especially when scaled for GPUs.
Innovation Solution
A hardware-based call stack attribution method that uses a state transition data structure to generate and update call stacks for each execution thread, associating a call stack identifier with branch identifiers and storing it in hardware registers, allowing for real-time tracking of application execution without performance penalties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If software profilers are used to measure application performance, then performance metrics can be obtained, but the application performance is altered due to invasive instrumentation
Solution Approach 1:
The patent replaces software-based instrumentation mechanisms with hardware-based call stack tracking mechanisms. The hardware call stack attribution logic and state transition data structure operate in the hardware domain, automatically tracking function calls without requiring software instrumentation, thereby eliminating the performance penalty associated with software profilers while maintaining measurement accuracy
2Measurement precision
If hardware-based techniques like LBR and BTS are used, then performance measurement is achieved, but the amount of stored performance information is limited
Solution Approach 1:
The patent transitions from the traditional CPU register-based call stack storage to a GPU-specific hardware dimension using dedicated call stack attribution logic and state transition data structures. This dimensional shift enables the system to track and store comprehensive call stack information for multiple threads and warps simultaneously, overcoming the storage limitations of conventional hardware techniques when scaled to GPU architectures
3Adaptability or versatility
If CPU hardware techniques are scaled to GPU, then performance measurement coverage is increased, but the overhead and inaccuracy increase dramatically
Solution Approach 1:
The patent segments the call stack tracking functionality into distinct hardware components within the GPU architecture: call stack attribution logic, state transition data structure, and per-thread call stack registers. This segmentation allows each component to operate independently and efficiently, managing the complexity of multi-thread performance tracking by dividing it into manageable, specialized hardware units that can be scaled across multiple GPU cores
Data Source
AI summary
Apparatuses, systems, and techniques for hardware-driven call stack attribution. The apparatuses, systems, and techniques includes generating and updating call stacks within a processing device during execution of an application. In particular, determining a branch identifier associated with an instruction being executed by an execution thread, identifying a call stack identifier of the execution thread executing the instruction, and updating the call stack identifier of the execution thread based on the identified call stack identifier of the execution thread and the branch identifier.