Hint-K: An Efficient Multi-level Cache Using K-Step Hints

Wu, Chongqing; He, Xubin; Cao, Qiang; Xie, Changsheng

doi:10.1109/icpp.2010.70

Cited by 13 publications

(22 citation statements)

References 13 publications

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“…A state-of-the-art implementation combining Karma and MC 2 is presented in [48]. Hint-K [22][23] uses the k-step hint history information of both demote and promote operations, which can efficiently describe the activeness of data blocks. In addition, with the development of tiered storage [49] , unified single level cache algorithms (e.g., UARC [17] ) are another way to provide a comprehensive solution for hybrid memory systems.…”

Section: Multilevel Cache Algorithmsmentioning

confidence: 99%

“…As LRU-K [27] and Hint-K [22] use k steps to record history information, HFA takes a long time interval (t 1 +t 2 = kT , k is an integer and T is a pre-defined time slot) to accurately evaluate the hint frequency value of data blocks. We set a hotness degradation value δ, and aggregate the hint frequency of block x as:…”

Section: Identification Of Hot Blocksmentioning

confidence: 99%

“…A hybrid hint is a flag containing information of a data block from multiple aspects such as demotion, promotion or application access patterns. In many previous literatures [20][21][22] , hybrid hints are demonstrated to be an efficient way to enhance the performance significantly.…”

Section: Introductionmentioning

confidence: 99%

“…And the application hints are usually dependent on a specified application, which is not general to all scenarios. Some algorithms [22][23] keep the latest multiple steps hint information, but they are insufficient to describe the status of data blocks. The detailed illustration is given in Subsection 2.3.…”

Section: Introductionmentioning

confidence: 99%

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A Hint Frequency Based Approach to Enhancing the I/O Performance of Multilevel Cache Storage Systems

Meng

Guo

et al. 2017

J. Comput. Sci. Technol.

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Meng XD, Wu CT, Guo MY et al. A hint frequency based approach to enhancing the I/O performance of multilevel cache storage systems. AbstractWith the enormous and increasing user demand, I/O performance is one of the primary considerations to build a data center. Several new technologies in data centers, such as tiered storage, prompt the widespread usage of multilevel cache techniques. In these storage systems, the upper level storage typically serves as a cache for the lower level, which forms a distributed multilevel cache system. However, although many excellent multilevel cache algorithms have been proposed to improve the I/O performance, they still have potential to be enhanced by investigating the history information of hints. To address this challenge, in this paper, we propose a novel hint frequency based approach (HFA), to improve the overall multilevel cache performance of storage systems. The main idea of HFA is using hint frequencies (the total number of demotions/promotions by employing demote/promote hints) to efficiently explore the valuable history information of data blocks among multiple levels. HFA can be applied with several popular multilevel cache algorithms, such as Demote, Promote and Hint-K. Simulation results show that, compared with original multilevel cache algorithms such as Demote, Promote and Hint-K, HFA can improve the I/O performance by up to 20% under different I/O workloads.

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Section: Multilevel Cache Algorithmsmentioning

confidence: 99%

Section: Identification Of Hot Blocksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Hint Frequency Based Approach to Enhancing the I/O Performance of Multilevel Cache Storage Systems

Meng

Guo

et al. 2017

J. Comput. Sci. Technol.

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“…Multi-level caching gains much research interest, especially in the emerging age of cloud computing where the hierarchy of (distributed) storage is being redefined with more layers. For example Hint-K [41] caching is proposed to keep track of the last K steps across all the cache levels, which generalizes the conventional LRU-K algorithm concerned only on the single level information.…”

Section: F Broader Impactmentioning

confidence: 99%

HyCache+: Towards Scalable High-Performance Caching Middleware for Parallel File Systems

Zhao

Qiao

Raicu

2014

2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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Abstract-The ever-growing gap between the computation and I/O is one of the fundamental challenges for future computing systems. This computation-I/O gap is even larger for modern large scale high-performance systems due to their state-of-the-art yet decades long architecture: the compute and storage resources form two cliques that are interconnected with shared networking infrastructure. This paper presents a distributed storage middleware, called HyCache+, right on the compute nodes, which allows I/O to effectively leverage the high bi-section bandwidth of the high-speed interconnect of massively parallel high-end computing systems. HyCache+ provides the POSIX interface to end users with the memory-class I/O throughput and latency, and transparently swap the cached data with the existing slowspeed but high-capacity networked attached storage. HyCache+ has the potential to achieve both high performance and lowcost large capacity, the best of both worlds. To further improve the caching performance from the perspective of the global storage system, we propose a 2-phase mechanism to cache the hot data for parallel applications, called 2-Layer Scheduling (2LS), which minimizes the file size to be transferred between compute nodes and heuristically replaces files in the cache. We deploy HyCache+ on the IBM BlueGene/P supercomputer, and observe two orders of magnitude faster I/O throughput than the default GPFS parallel file system. Furthermore, the proposed heuristic caching approach shows 29X speedup over the traditional LRU algorithm.

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Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents

Wong

Gur

et al. 2020

JAMA Netw Open

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IMPORTANCE Chest radiography is the most common diagnostic imaging examination performed in emergency departments (EDs). Augmenting clinicians with automated preliminary read assistants could help expedite their workflows, improve accuracy, and reduce the cost of care. OBJECTIVE To assess the performance of artificial intelligence (AI) algorithms in realistic radiology workflows by performing an objective comparative evaluation of the preliminary reads of anteroposterior (AP) frontal chest radiographs performed by an AI algorithm and radiology residents. DESIGN, SETTING, AND PARTICIPANTS This diagnostic study included a set of 72 findings assembled by clinical experts to constitute a full-fledged preliminary read of AP frontal chest radiographs. A novel deep learning architecture was designed for an AI algorithm to estimate the findings per image. The AI algorithm was trained using a multihospital training data set of 342 126 frontal chest radiographs captured in ED and urgent care settings. The training data were labeled from their associated reports. Image-based F1 score was chosen to optimize the operating point on the receiver operating characteristics (ROC) curve so as to minimize the number of missed findings and overcalls per image read. The performance of the model was compared with that of 5 radiology residents recruited from multiple institutions in the US in an objective study in which a separate data set of 1998 AP frontal chest radiographs was drawn from a hospital source representative of realistic preliminary reads in inpatient and ED settings. A triple consensus with adjudication process was used to derive the ground truth labels for the study data set. The performance of AI algorithm and radiology residents was assessed by comparing their reads with ground truth findings. All studies were conducted through a web-based clinical study application system. The triple consensus data set

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Hint-K: An Efficient Multi-level Cache Using K-Step Hints

Cited by 13 publications

References 13 publications

A Hint Frequency Based Approach to Enhancing the I/O Performance of Multilevel Cache Storage Systems

A Hint Frequency Based Approach to Enhancing the I/O Performance of Multilevel Cache Storage Systems

HyCache+: Towards Scalable High-Performance Caching Middleware for Parallel File Systems

Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents

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