Cache management policies should consider workloads’ contention behavior when managing a shared cache. Prior art makes estimates about shared cache behavior by adding extra logic or time to isolate per workload cache statistics. These approaches provide per-workload analysis but do not provide a holistic understanding of the utilization and effectiveness of caches under the ever-growing contention that comes standard with scaling cores. We present Contention Analysis in Shared Hierarchies using Thefts, or CASHT, 1 a framework for capturing cache contention information both offline and online. CASHT takes advantage of cache statistics made richer by observing a consequence of cache contention: inter-core evictions, or what we call THEFTS. We use thefts to complement more familiar cache statistics to train a learning model based on Gradient-boosting Trees (GBT) to predict the best ways to partition the last-level cache. GBT achieves 90+% accuracy with trained models as small as 100 B and at least 95% accuracy at 1 kB model size when predicting the best way to partition two workloads. CASHT employs a novel run-time framework for collecting thefts-based metrics despite partition intervention, and enables per-access sampling rather than set sampling that could add overhead but may not capture true workload behavior. Coupling CASHT and GBT for use as a dynamic policy results in a very lightweight and dynamic partitioning scheme that performs within a margin of error of Utility-based Cache Partitioning at a 1/8 the overhead.

Recent years see cache partitioning techniques employ commercial way partitioning to great effect. Techniques like partitioning build analysis on miss curves. However, the various forms of partitioning run into the issue of core scaling, requiring multi-tenant partitions and analysis of how workloads behave when sharing ever shrinking resources. We observe surprising variations in performance which can be disjoint with variations in cache misses that encourage such analysis. We present Capacity Curve Annotation and Feature Extraction, methodologies for annotation, feature extraction, and analysis of sensitivity studies based on performance curves.

The "memory wall" in CPU design refers to increasing divergence in performance growth between processors and memory. Prefetching and cache replacement were developed to overcome this, but there are few evaluations of the two techniques being used together. We contribute to this space by evaluating modern replacement policies in the context of a stride-N prefetcher. In exposing combative behavior between modern policies and the prefetcher, we hope to inform future design choices regarding such integration. Further, we present a means to either recoup or surpass performance gains seen in isolation for policies that exhibit conflict.

The more complete the training set of an optical character recognition platform, the greater the chances of obtaining a better precision in transcription. The development of a database for such purpose is a task of paramount effort as it is performed manually and must be as extensive as possible in order to potentially cover all words in a language. Dealing with historic documents either handwritten, typed, or printed is even a harder effort as documents are often degraded by time and storage conditions. The recent work of Silva-Lins showed how to automatically generate training sets of isolated characters for cursive writing of one specific person. This is particularly important in the transcription of historic files of important people. The present work improves that strategy by analyzing letter ligature patterns. The improvement in OCR transcription accuracy both of printed, typed and handwritten documents is borne out by experimental evidence.