Scalar Interpolation in Vectorized Loops for Processor Stall Reduction
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Solution Overview
Problem
Modern computing systems face inefficiencies in resource utilization, leading to reduced performance and increased processor stalls due to overuse or underuse of resources, particularly in dynamically scheduled out-of-order processors with high register pressure and distinct memory configurations for scalar and vector values.
Innovation Solution
The method involves scalar interpolation, where scalar iterations are interpolated into vectorized loops to balance resource utilization, using cost models and machine-learned models to determine the optimal number of scalar iterations and scheduling, thereby optimizing resource usage and improving performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If vectorization is applied to maximize data level parallelism, then data level parallelism is improved, but scalar resources become underutilized and processor stalls increase
Solution Approach 1:
The patent segments the loop iterations into vectorized iterations (for data level parallelism) and scalar iterations (for resource balancing). By dividing the total iterations into different categories and handling them separately, the system achieves both high data level parallelism through vectorization and prevents processor stalls by injecting scalar operations to keep scalar resources busy, thus resolving the contradiction between maximizing data level parallelism and preventing processor stalls.
Solution Approach 2:
The patent applies local quality by treating different parts of the loop differently: vectorized iterations are processed with high-level parallelism for data-dependent operations, while scalar iterations are injected at specific locations to balance resource utilization. This localized differentiation allows the system to optimize both data level parallelism in the vectorized sections and resource utilization in the scalar sections, resolving the contradiction between these two objectives.
2Reliability
If scalar iterations are interpolated into vectorized loops to balance resource utilization, then resource utilization is improved, but instruction complexity increases
Solution Approach 1:
The patent applies partial action by interpolating only a specific number of scalar iterations (not all iterations) into the vectorized loops. The number of scalar iterations is calculated based on available scalar resources and loop characteristics, injecting just enough scalar operations to balance resource utilization without excessively increasing instruction complexity. This partial approach resolves the contradiction by achieving resource balance while minimizing the added complexity.
Solution Approach 2:
The patent uses parameter changes by dynamically adjusting the number of scalar iterations to interpolate based on parameters such as available scalar resources, loop iteration count, and resource utilization metrics. By changing the interpolation parameter (number of scalar iterations) based on system state, the system optimizes resource utilization while controlling instruction complexity, resolving the contradiction between these two objectives.
3Reliability
If multiple scalar iterations are interpolated into each vectorized loop, then resource utilization is improved, but the number of instructions increases
Solution Approach 1:
The patent applies dynamics by making the number of interpolated scalar iterations variable rather than fixed. The system dynamically calculates the optimal number of scalar iterations to interpolate based on real-time or static analysis of scalar resource availability and loop characteristics. This dynamic adjustment allows the system to maximize resource utilization while minimizing the increase in instruction count, resolving the contradiction between resource utilization and instruction count.
Solution Approach 2:
The patent employs feedback mechanisms where the number of scalar iterations to interpolate is determined based on feedback about scalar resource utilization patterns and availability. By using feedback from resource monitoring or profiling data, the system adjusts the interpolation parameter to achieve optimal resource utilization with minimal instruction overhead, resolving the contradiction between resource utilization and instruction count.
Data Source
AI summary
Methods and systems for balancing computing resources is described. According to an aspect, a method for balancing computing resources may be provided. The method includes generating multiple vectorized loops of a scalar code. The method may further include interleaving each of the multiple vectorized loops to generate multiple interleaved vectorized loops. The interleaving may be based on available vector resources. The method may further include interpolating one or more scalar iterations of the scalar loop into each of the multiple interleaved vectorized loops to generate multiple interleaved and scalar interpolated vectorized loops. The method may further include selecting one of the multiple interleaved and scalar interpolated vectorized loops based on a cost mode.