SEARCH
ISTC-CC NEWSLETTER
RESEARCH HIGHLIGHTS
Ling Liu's SC13 paper "Large Graph Processing Without the Overhead" featured by HPCwire.
ISTC-CC provides a listing of useful benchmarks for cloud computing.
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.
ISTC-CC Abstract
MICA: A Holistic Approach to Near-Line-Rate In-Memory Key-Value Caching on General-Purpose Hardware
11th USENIX Symposium on Networked Systems Design and Implementation (NSDI'14), April 2014.
Hyeontaek Lim, Dongsu Han*, David G. Andersen, Michael E. Kaminsky^
Carnegie Mellon University,
*KAIST,
^Intel Labs
Systems Design and Implementation (NSDI'14), April 2014. MICA is a scalable in-memory key-value store that handles 65.6 to 76.9 million key-value operations per second using a single general-purpose multi-core system. MICA is over 4–13.5x faster than current state-of-the-art systems, while providing consistently high throughput over a variety of mixed read and write workloads.
MICA takes a holistic approach that encompasses all aspects of request handling, including parallel data access, network request handling, and data structure design, but makes unconventional choices in each of the three domains. First, MICA optimizes for multi-core architectures by enabling parallel access to partitioned data. Second, for efficient parallel data access, MICA maps client requests directly to specific CPU cores at the server NIC level by using client-supplied information and adopts a light-weight networking stack that bypasses the kernel. Finally, MICA’s new data structures—circular logs, lossy concurrent hash indexes, and bulk chaining—handle both read- and write-intensive workloads at low overhead.
FULL PAPER: pdf
