Deterministic GPU Trace Replay Through CPU Emulation
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Solution Overview
Problem
There is a lack of effective tools for workload profiling and analysis on graphics processing units (GPUs) due to their separate environment and limited OS support, as well as the complexity of managing hundreds or thousands of parallel software threads, complicating deterministic replay of multi-threaded traces.
Innovation Solution
A method for generating a self-contained GPU trace (GLIT) and using a fast functional emulator (GTReplay) to replay these traces deterministically on a CPU, ensuring synchronization and determinism by maintaining the order of Events such as memory accesses and inter-thread synchronization, without relying on memory-resident data or thread synchronization during trace logging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPU profiling tools are developed to enable workload analysis, then debugging and validation capabilities are improved, but device complexity and difficulty of implementation increase due to the separate GPU environment and limited OS support
Solution Approach 1:
The patent introduces an intermediary tracing mechanism that captures GPU execution events and translates them into a standardized trace format. This intermediary layer bridges the gap between the GPU environment and CPU-based analysis tools, enabling profiling without requiring complex GPU-specific debugging infrastructure. The trace buffer and event recording system act as mediators that convert GPU internal states into analyzable data structures.
Solution Approach 2:
The patent creates a simplified copy of the GPU execution model that can be replayed on the CPU. Instead of directly analyzing the complex GPU environment, the system generates trace records that replicate the essential execution flow and state changes. This copied representation allows standard CPU-based debugging and analysis tools to be applied to GPU workloads without modifying the GPU hardware or firmware.
2Stability of the object's composition
If deterministic replay is implemented for multi-threaded GPU traces, then analysis reproducibility is improved, but synchronization complexity increases due to hundreds or thousands of parallel software threads
Solution Approach 1:
The patent segments the multi-threaded GPU execution trace into individual thread traces, each representing the execution flow of a single software thread. By dividing the complex multi-threaded trace into manageable thread-level segments, the system can apply deterministic replay techniques to each thread independently while maintaining overall execution order through event sequencing. This segmentation reduces the synchronization problem from managing hundreds of threads simultaneously to managing individual thread sequences.
Solution Approach 2:
The patent performs preliminary ordering of events during the trace capture phase, assigning sequence numbers and establishing the chronological order of GPU events before replay. This preliminary action creates a deterministic event queue that guides the replay process, ensuring that events are reexecuted in the same order as the original execution. By pre-establishing the event sequence, the system avoids the need for complex runtime synchronization during replay.
3Productivity
If trace recording is performed without memory-resident data, then profiling overhead is reduced, but information completeness may be compromised
Solution Approach 1:
The patent extracts only the essential execution events and state changes from the GPU memory system, recording them in a compact trace format. Instead of capturing all memory-resident data, the system selectively extracts events such as instruction executions, memory access patterns, and synchronization points. This extraction approach maintains the critical information needed for deterministic replay and analysis while minimizing the overhead of data collection and storage.
Solution Approach 2:
The patent changes the representation parameters of trace data from detailed memory-resident information to event-based abstract representations. By transforming concrete memory states into event records with sequence numbers, thread identifiers, and operation types, the system achieves a balance between information completeness and recording efficiency. The parameter transformation allows the trace to capture execution semantics without the burden of full memory state capture.
Data Source
AI summary
A deterministic replay of a multi-threaded trace on a multi-threaded processor is described. An example of a computer-readable storage medium includes instructions to cause at least one processor to receive graphics processing unit (GPU) program code for tracing, the program code including a plurality of instructions; analyze the plurality of instructions to identify instructions of the program code that are events requiring synchronization; instrument each of the identified events to generate instrumented program code; execute the instrumented program code on a plurality of hardware threads of the GPU to generate trace data; and emulate the trace data utilizing an emulator on a plurality of hardware traces of a central processing unit (CPU), including replaying the identified events according to an order of occurrence of the identified events.