Identifying and Exploiting Sparse Branch Correlations for Optimizing Branch Prediction

TL;DR

This paper reveals that branch correlations are often sparse and can be efficiently modeled, leading to a new sparsity-aware prediction method that improves accuracy with minimal overhead.

Contribution

It introduces a novel sparsity-aware branch prediction mechanism that exploits sparse correlations for improved accuracy and efficiency.

Findings

Achieves up to 42% reduction in MPKI

Operates within acceptable latency and power constraints

Demonstrates effectiveness across various design parameters

Abstract

Branch prediction is arguably one of the most important speculative mechanisms within a high-performance processor architecture. A common approach to improve branch prediction accuracy is to employ lengthy history records of previously seen branch directions to capture distant correlations between branches. The larger the history, the richer the information that the predictor can exploit for discovering predictive patterns. However, without appropriate filtering, such an approach may also heavily disorganize the predictor's internal mechanisms, leading to diminishing returns. This paper studies a fundamental control-flow property: the sparsity in the correlation between branches and recent history. First, we show that sparse branch correlations exist in standard applications and, more importantly, such correlations can be computed efficiently using sparse modeling methods. Second, we introduce a sparsity-aware branch prediction mechanism that can compactly encode and store sparse models to unlock essential performance opportunities. We evaluated our approach for various design parameters demonstrating MPKI improvements of up to 42% (2.3% on average) with 2KB of additional storage overhead. Our circuit-level evaluation of the design showed that it can operate within accepted branch prediction latencies, and under reasonable power and area limitations.