SHARP: SHARing-Aware Cache Writeback byPass

In modern multi-processor systems-on-chips (MPSoCs), writebacks from the private caches to the shared cache can introduce significant performance bottlenecks, especially because multiple threads from different co-executing programs contend for the shared cache resources. Intelligent cache bypass decisions for writebacks help mitigate such contention and enhance the utilization of the shared cache. Most prior cache bypass strategies account for contention for shared cache capacity by focusing primarily on data reuse, with only recent research beginning to consider bandwidth contention also in dynamic bypass decisions. However, data sharing, a crucial characteristic of modern multithreaded workloads, remains largely overlooked by state-of-the-art cache bypass decisions. Bypassing highly shared cache lines can increase the volume of main memory accesses, potentially resulting in performance bottlenecks. We introduce SHARP, a novel cache bypass policy that incorporates three key factors: data sharing, contention, and data reuse, into its dynamic bypass decisions for cache writebacks. In addition to prioritizing the caching of data with high reuse, we prioritize the caching of data shared across multiple threads to enhance cache utilization. We dynamically modulate our bypass decisions, employing aggressive bypass for writebacks when shared cache contention is high, while employing conservative bypass when contention is low. Experiments across a diverse set of PARSEC workloads demonstrate that SHARP improves overall system throughput by 12% and 8% compared to the no-bypass baseline and the state-of-the-art bypass baseline, respectively. SHARP also reduces the overall cache energy consumption by 14% over the no-bypass baseline.

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Publication Details

Journal
ACM Transactions on Embedded Computing Systems
Published
2025-08-16
DOI
https://doi.org/10.1145/3760746
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
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article

SHARP: SHARing-Aware Cache Writeback byPass

Preeti Ranjan Panda, Dinesh Joshi, Aritra Bagchi
ACM Transactions on Embedded Computing Systems
Parallel Computing and Optimization Techniques
article

SHARP: SHARing-Aware Cache Writeback byPass

Preeti Ranjan Panda, Dinesh Joshi, Aritra Bagchi
article en

Abstract

In modern multi-processor systems-on-chips (MPSoCs), writebacks from the private caches to the shared cache can introduce significant performance bottlenecks, especially because multiple threads from different co-executing programs contend for the shared cache resources. Intelligent cache bypass decisions for writebacks help mitigate such contention and enhance the utilization of the shared cache. Most prior cache bypass strategies account for contention for shared cache capacity by focusing primarily on data reuse, with only recent research beginning to consider bandwidth contention also in dynamic bypass decisions. However, data sharing, a crucial characteristic of modern multithreaded workloads, remains largely overlooked by state-of-the-art cache bypass decisions. Bypassing highly shared cache lines can increase the volume of main memory accesses, potentially resulting in performance bottlenecks. We introduce SHARP, a novel cache bypass policy that incorporates three key factors: data sharing, contention, and data reuse, into its dynamic bypass decisions for cache writebacks. In addition to prioritizing the caching of data with high reuse, we prioritize the caching of data shared across multiple threads to enhance cache utilization. We dynamically modulate our bypass decisions, employing aggressive bypass for writebacks when shared cache contention is high, while employing conservative bypass when contention is low. Experiments across a diverse set of PARSEC workloads demonstrate that SHARP improves overall system throughput by 12% and 8% compared to the no-bypass baseline and the state-of-the-art bypass baseline, respectively. SHARP also reduces the overall cache energy consumption by 14% over the no-bypass baseline.

ACM Transactions on Embedded Computing SystemsVol. 24(5s)
Indian Institute of Technology Delhi (IN)
Openalex Percentile: Top 77%
Parallel Computing and Optimization Techniques
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SHARP: SHARing-Aware Cache Writeback byPass — Preeti Ranjan Panda, Dinesh Joshi, et al. · ACM Transactions on Embedded Computing Systems (2025) | TGRS Research Map | TGRS