Ling Liu's SC13 paper "Large Graph Processing Without the Overhead" featured by HPCwire.
Another list highlighting Open Source Software Releases.
Second GraphLab workshop should be even bigger than the first! GraphLab is a new programming framework for graph-style data analytics.
TABLEFS: Enhancing Metadata Efficiency in the Local
IEEE Asia-Pacific Magnetic Recording Conference (APMRC), November 2012. Also published as Carnegie Mellon University Parallel Data Laboratory Technical Report CMU-PDL-12-110, Sept., 2012.
Kai Ren, Garth Gibson
Carnegie Mellon University
File systems that manage magnetic disks have long recognized the importance of sequential allocation and large transfer sizes for file data. Fast random access has dominated metadata lookup data structures with increasingly use of B-trees on-disk. For updates, on-disk data structures are increasingly non-overwrite, copy-on-write, log-like and deferred. Yet our experiments with workloads dominated by metadata and small file access indicate that even sophisticated local disk file systems like Ext4, XFS and BTRFS leaves a lot of opportunity for performance improvement in workloads dominated by metadata and small files.
In this paper we present a simple stacked file system, TableFS, which uses another local file system as an object store and organizes all metadata into a single sparse table backed on-disk using a Log-Structured Merge (LSM) tree, LevelDB in our experiments. By stacking, TableFS asks only for efficient large file allocation and access from the local file system. By using an LSM tree, TableFS ensures metadata is written to disk in large, non-overwrite, sorted and indexed logs, and inherits a compaction algorithm. Even an inefficient FUSE based user level implementation of TableFS can perform comparably to Ext4, XFS and BTRFS on simple dataintensive benchmarks, and can outperform them by 50% to as much as 1000% for a metadata-intensive query/update workload on data-free files. Such promising performance results from TableFS suggest that local disk file systems can be significantly improved by much more aggressive aggregation and batching of metadata updates.
KEYWORDS: TableFS, File System, File System Metadata, NoSQL Database, LSM Tree
FULL PAPER: pdf