Parallel Processor Metric Measurement with Segmented Sequences
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Solution Overview
Problem
Measuring multiple metrics concurrently for parallel processing units like GPUs consumes significant processing power, leading to overhead and reduced efficiency in performance measurement.
Innovation Solution
Concurrently measure a sequence of metrics for each processor, where each sequence starts with a subsequent metric to the previous one, reducing simultaneous measurement load and optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple metrics are measured concurrently for each GPU, then comprehensive performance monitoring is achieved, but processing power consumption increases significantly
Solution Approach 1:
The patent divides the measurement of multiple metrics into sequential segments rather than simultaneous operations. Each GPU measures metrics one after another in a defined sequence, where GPU 0 measures metric 0, then GPU 1 measures metric 1, and so on. This segmentation allows comprehensive monitoring of multiple metrics across multiple GPUs without requiring all measurements to occur simultaneously, thereby reducing the processing power consumption at any given moment while maintaining complete performance data collection.
2Loss of information
If multiple metrics are measured simultaneously for each processor, then complete metric data is collected, but measurement overhead increases
Solution Approach 1:
The patent segments the metric measurement process into distinct time slots and processor assignments. Instead of all processors measuring all metrics simultaneously, each processor is assigned a specific metric to measure at a specific time according to a predetermined sequence. This segmentation eliminates redundant simultaneous measurement operations, reducing measurement overhead while ensuring that every metric is measured by the appropriate processor to maintain data completeness.
Solution Approach 2:
The patent implements periodic measurement cycles where metrics are measured in a repeating sequence pattern. Each processor measures its assigned metric periodically according to the sequence, allowing complete metric data collection through multiple cycles. This periodic action distributes the measurement load over time rather than concentrating it simultaneously, reducing overhead while maintaining comprehensive data collection through repeated sampling.
3Measurement precision
If concurrent measurement of multiple metrics is implemented, then comprehensive performance data is obtained, but system complexity increases
Solution Approach 1:
The patent simplifies the measurement system by segmenting the measurement responsibilities among processors. Each processor is assigned a specific role in measuring a specific metric at a specific time, eliminating the need for complex coordination mechanisms to manage simultaneous measurements. This segmentation reduces system complexity by distributing simple, well-defined tasks rather than managing complex concurrent operations, while still obtaining comprehensive performance data through the coordinated sequence.
Data Source
AI summary
A method includes measuring a plurality of n metrics for a plurality of m processors executing portions of a workload in parallel. The measuring includes measuring, for a first processor of the plurality of m processors, a plurality of n metrics in a first sequence of a plurality of n sequences. The first sequence begins with a starting metric of the plurality of n metrics and ends with an nth metric. The measuring also includes measuring, for each remaining processor of the plurality of m processors, the plurality of n metrics in a sequence of the plurality of n sequences. Each progressive sequence of the plurality of n sequences begins with a progressive metric of the plurality of n metrics immediately subsequent to an immediately preceding sequence's starting metric of the plurality of n metrics. The method includes reporting the plurality of n metrics.