Utility-based Cache Partitioning Research Articles

CASHT: Contention Analysis in Shared Hierarchies with Thefts

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.

Synergy: A Hypervisor Managed Holistic Caching System

Efficient system-wide memory management is an important challenge for over-commitment based hosting in virtualized systems. Due to the limitation of memory domains considered for sharing, current deduplication solutions simply cannot achieve system-wide deduplication. Popular memory management techniques like sharing and ballooning enable important memory usage optimizations individually. However, they do not complement each other and, in fact, may degrade individual benefits when combined. We propose $\mathsf{Synergy}$Synergy, a hypervisor managed caching system to improve memory efficiency in over-commitment scenarios. $\mathsf{Synergy}$Synergy builds on an exclusive caching framework to achieve, for the first time, system-wide memory deduplication. $\mathsf{Synergy}$Synergy also enables the co-existence of the mutually agnostic ballooning and sharing techniques within hypervisor managed systems. Finally, $\mathsf{Synergy}$Synergy implements a novel file-level eviction policy that prevents hypervisor caching benefits from being squandered away due to partial cache hits. $\mathsf{Synergy}$Synergy's cache is flexible with configuration knobs for cache sizing and data storage options, and a utility-based cache partitioning scheme. Our evaluation shows that $\mathsf{Synergy}$Synergy consistently uses 10 to 75 percent lesser memory by exploiting system-wide deduplication as compared to inclusive caching techniques and achieves application speedup of 2x to 23x. We also demonstrate the capabilities of $\mathsf{Synergy}$Synergy to increase VM packing density and support for dynamic reconfiguration of cache partitioning policies.

Access Adaptive and Thread-Aware Cache Partitioning in Multicore Systems

Cache partitioning is a successful technique for saving energy for a shared cache and all the existing studies focus on multi-program workloads running in multicore systems. In this paper, we are motivated by the fact that a multi-thread application generally executes faster than its single-thread counterpart and its cache accessing behavior is quite different. Based on this observation, we study applications running in multi-thread mode and classify data of the multi-thread applications into shared and private categories, which helps reduce the interferences among shared and private data and contributes to constructing a more efficient cache partitioning scheme. We also propose a hardware structure to support these operations. Then, an access adaptive and thread-aware cache partitioning (ATCP) scheme is proposed, which assigns separate cache portions to shared and private data to avoid the evictions caused by the conflicts from the data of different categories in the shared cache. The proposed ATCP achieves a lower energy consumption, meanwhile improving the performance of applications compared with the least recently used (LRU) managed, core-based evenly partitioning (EVEN) and utility-based cache partitioning (UCP) schemes. The experimental results show that ATCP can achieve 29.6% and 19.9% average energy savings compared with LRU and UCP schemes in a quad-core system. Moreover, the average speedup of multi-thread ATCP with respect to single-thread LRU is at 1.89.

Dynamic Program Behavior Identification for High Performance CMPs with Private LLCs

Chip Multi-Processors (CMPs) emerge as a mainstream architectural design alternative for high performance parallel and distributed computing. Last Level Cache (LLC) management is critical to CMPs because off-chip accesses often require a long latency. Due to its short access latency, well performance isolation and easy scalability, private cache is an attractive design alternative for LLC of CMPs. This paper proposes program Behavior Identification-based Cache Sharing (BICS) for LLC management. BICS is based on a private cache organization for the shorter access latency. Meanwhile, BICS tries to simulate a shared cache organization by allowing evicted blocks of one private LLC to be saved at peer LLCs. This technique is called spilling. BICS identifies cache behavior types of applications at runtime. When a cache block is evicted from a private LLC, cache behavior characteristics of the local application are evaluated so as to determine whether the block is to be spilled. Spilled blocks are allowed to replace some valid blocks of the peer LLCs as long as the interference is within a reasonable level. Experimental results using a full system CMP simulator show that BICS improves the overall throughput by as much as 14.5%, 12.6%, 11.0%and 11.7% (on average 8.8%, 4.8%, 4.0% and 6.8%) over private cache, shared cache, Utility-based Cache Partitioning (UCP) scheme and the baseline spilling-based organization Cooperative Caching (CC) respectively on a 4-core CMP for SPEC CPU2006 benchmarks.