Runtime Profiler Instrumentation for Reconfigurable Dataflow Bottlenecks
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Solution Overview
Problem
Existing systems face challenges in accurately profiling and optimizing the performance of dataflow reconfigurable systems, particularly in identifying and debugging performance bottlenecks in complex applications like machine learning and artificial intelligence workloads, due to the asynchronous and parallel nature of thread execution and data exchange, which traditional compilers are ill-equipped to handle.
Innovation Solution
A profiler is developed to instrument dataflow pipelines on reconfigurable processors, using hardware counters and compile-time logic to measure performance metrics such as stage latency, and provide recommendations for optimization by analyzing collected data, tailored to different workload types and distribution patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional compilers are used for profiling reconfigurable processors, then device complexity is reduced, but measurement precision and ability to identify performance bottlenecks deteriorate
Solution Approach 1:
The patent introduces a specialized profiler as an intermediary component between the reconfigurable processor and traditional compilation tools. This profiler includes instrumentation logic that inserts performance monitoring code into the dataflow graph, hardware performance counters that collect runtime metrics, and software analysis tools that process the collected data. This intermediary system enables precise bottleneck identification without requiring changes to the core reconfigurable processor architecture.
Solution Approach 2:
The profiler system is segmented into multiple independent components: (1) instrumentation logic that modifies the dataflow graph, (2) hardware performance counters that collect metrics, (3) runtime systems that manage execution, and (4) analysis tools that process data. This segmentation allows each component to be optimized independently and reduces the complexity burden on any single part of the system.
2Measurement precision
If hardware performance counters are implemented to collect detailed metrics, then measurement precision improves, but device complexity and resource usage increase
Solution Approach 1:
The profiler implements partial monitoring by allowing users to select specific performance metrics and dataflow graph regions to monitor. Instead of collecting all possible metrics across the entire system, the profiler focuses only on the subset of metrics relevant to the specific performance bottleneck being investigated. This selective approach reduces the activation and resource usage of hardware performance counters while maintaining measurement precision for the targeted metrics.
3Measurement precision
If instrumentation logic is used to insert performance monitoring code, then measurement precision improves, but device complexity and compilation complexity increase
Solution Approach 1:
The instrumentation logic performs preliminary actions during the compilation phase by automatically inserting performance monitoring code into the dataflow graph before execution. This pre-instrumentation includes placing measurement points at critical locations in the dataflow, configuring hardware counters with appropriate parameters, and generating the necessary runtime support code. By performing these actions during compilation rather than at runtime, the system achieves high measurement precision without complicating the runtime execution environment.
Data Source
AI summary
A data processing system comprises profiler logic to determine profiling parameters that specify instrumentation for performance data generation in compiled instructions for a dataflow graph of an application to be executed on a reconfigurable data processing unit including an array of processing units. The profiling parameters may be determined based on a workload type of a portion of the dataflow graph and one or more profiling modes for the portion of the dataflow graph. The profiling modes may specify respective sets of operational components of the portion of the dataflow graph for which performance data is to be collected.