bLSM

Sears, Russell; Ramakrishnan, Raghu

doi:10.1145/2213836.2213862

Cited by 231 publications

(7 citation statements)

References 19 publications

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“…The ETL is a function of expected 0D and 1D barcodes. The expected 0D barcode can also be viewed as the expected heights of branching in a random merge tree [90][91][92][93][94][95].…”

Section: Inference On Expected Birth and Death Valuesmentioning

confidence: 99%

Topological data analysis of human brain networks through order statistics

Das

Anand²,

Chung³

2023

PLoS ONE

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Understanding the common topological characteristics of the human brain network across a population is central to understanding brain functions. The abstraction of human connectome as a graph has been pivotal in gaining insights on the topological properties of the brain network. The development of group-level statistical inference procedures in brain graphs while accounting for the heterogeneity and randomness still remains a difficult task. In this study, we develop a robust statistical framework based on persistent homology using the order statistics for analyzing brain networks. The use of order statistics greatly simplifies the computation of the persistent barcodes. We validate the proposed methods using comprehensive simulation studies and subsequently apply to the resting-state functional magnetic resonance images. We found a statistically significant topological difference between the male and female brain networks.

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“…The ETL is a function of expected 0D and 1D barcodes. The expected 0D barcode can also be viewed as the expected heights of branching in a random merge tree [90][91][92][93][94][95].…”

Section: Inference On Expected Birth and Death Valuesmentioning

confidence: 99%

Topological data analysis of human brain networks through order statistics

Das

Anand²,

Chung³

2023

PLoS ONE

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“…Note that it is impossible to partially update an entry in SortedStore, thus the merge operation from HashStore to SortedStore always involves a full revision of the entire index. bLSM 11 improves the overall read performance of LSM‐trees by adopting Bloom filters and suggests a new compaction scheduler called Spring and Gear , to bound the write latency without impacting the overall throughput. ForestDB 27 uses HB+‐trie to index long keys, effectively improving the read performance and reducing the space overhead.…”

Section: Background and Motivationmentioning

confidence: 99%

“…For I/O‐intensive workloads, the KV stores based on log‐structured merge‐trees (LSM‐trees) 10 have been extensively research and widely deployed 2,8,11‐15 . The main advantage of the LSM‐trees over other indexing structures (such as B‐trees) is that they can deliver high performance for sequential (batch KV pairs) write access patterns on either solid‐state drives (SSDs) or hard‐disk drives 16 by maintaining the ordered keys and values for compaction at different levels in background.…”

Section: Introductionmentioning

confidence: 99%

Improving LSM‐trie performance by parallel search

et al. 2020

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Summary LSM‐trie‐based key‐value (KV) store is often used to manage an ultralarge dataset in reality by introducing a number of sublevels at each level, its linear growth pattern can fairly reduce the write amplification in store operations. Although this design is effective for the write operation, the last level holds a large proportion of KV items, leading to the extreme imbalance of data distribution. Therefore, to support efficient read, we need to carefully consider this imbalance. On the other hand, to ensure that acquired data is latest, the LSM‐trie needs to search the dataset at different levels one by one, and this search method may take a lot of unnecessary time. When the number of items is ultralarge, the random lookup performance may be poor due to the imbalance data distribution. To address this issue, we improve the read performance of the LSM‐trie by changing its serial search to parallel search, using two threads to simultaneously search at the last level and other levels, respectively. Our experiment results show that the read performance of the LSM‐trie can be improved up to 98.35% and on average 71.55%.

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“…This enables queries to quickly rule out regions in the storage that do not contain a key and directly jump to the data of interest. To dynamically sort data by key as data is written to storage, one uses a self-balancing data structure, such as an LSM-Tree [68,81]. Figure 6 shows the internal workings of a simplified LSM-Tree.…”

Section: Indexing Data In a Single Passmentioning

confidence: 99%

Streaming Data Reorganization at Scale with DeltaFS Indexed Massive Directories

et al. 2020

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Complex storage stacks providing data compression, indexing, and analytics help leverage the massive amounts of data generated today to derive insights. It is challenging to perform this computation, however, while fully utilizing the underlying storage media. This is because, while storage servers with large core counts are widely available, single-core performance and memory bandwidth per core grow slower than the core count per die. Computational storage offers a promising solution to this problem by utilizing dedicated compute resources along the storage processing path. We present DeltaFS Indexed Massive Directories (IMDs), a new approach to computational storage. DeltaFS IMDs harvest available (i.e., not dedicated) compute, memory, and network resources on the compute nodes of an application to perform computation on data. We demonstrate the efficiency of DeltaFS IMDs by using them to dynamically reorganize the output of a real-world simulation application across 131,072 CPU cores. DeltaFS IMDs speed up reads by 1,740× while only slightly slowing down the writing of data during simulation I/O for in situ data processing.

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bLSM

Cited by 231 publications

References 19 publications

Topological data analysis of human brain networks through order statistics

Topological data analysis of human brain networks through order statistics

Improving LSM‐trie performance by parallel search

Streaming Data Reorganization at Scale with DeltaFS Indexed Massive Directories

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