Browsing by Subject "Cache"

Now showing 1 - 2 of 2

Open Access
Cache hierarchy-aware query mapping on emerging multicore architectures
(IEEE, 2017) Öztürk, Özcan; Orhan, U.; Ding, W.; Yedlapalli, P.; Kandemir, M. T.
One of the important characteristics of emerging multicores/manycores is the existence of 'shared on-chip caches,' through which different threads/processes can share data (help each other) or displace each other's data (hurt each other). Most of current commercial multicore systems on the market have on-chip cache hierarchies with multiple layers (typically, in the form of L1, L2 and L3, the last two being either fully or partially shared). In the context of database workloads, exploiting full potential of these caches can be critical. Motivated by this observation, our main contribution in this work is to present and experimentally evaluate a cache hierarchy-aware query mapping scheme targeting workloads that consist of batch queries to be executed on emerging multicores. Our proposed scheme distributes a given batch of queries across the cores of a target multicore architecture based on the affinity relations among the queries. The primary goal behind this scheme is to maximize the utilization of the underlying on-chip cache hierarchy while keeping the load nearly balanced across domain affinities. Each domain affinity in this context corresponds to a cache structure bounded by a particular level of the cache hierarchy. A graph partitioning-based method is employed to distribute queries across cores, and an integer linear programming (ILP) formulation is used to address locality and load balancing concerns. We evaluate our scheme using the TPC-H benchmarks on an Intel Xeon based multicore. Our solution achieves up to 25 percent improvement in individual query execution times and 15-19 percent improvement in throughput over the default Linux-based process scheduler. © 1968-2012 IEEE.
Open Access
Graph aware caching policy for distributed graph stores
(IEEE, 2015-03) Aksu, Hidayet; Canım, M.; Chang, Y.-C.; Körpeoğlu, İbrahim; Ulusoy, Özgür
Graph stores are becoming increasingly popular among NOSQL applications seeking flexibility and heterogeneity in managing linked data. Conceptually and in practice, applications ranging from social networks, knowledge representations to Internet of things benefit from graph data stores built on a combination of relational and non-relational technologies aimed at desired performance characteristics. The most common data access pattern in querying graph stores is to traverse from a node to its neighboring nodes. This paper studies the impact of such traversal pattern to common data caching policies in a partitioned data environment where a big graph is distributed across servers in a cluster. We propose and evaluate a new graph aware caching policy designed to keep and evict nodes, edges and their metadata optimized for query traversal pattern. The algorithm distinguishes the topology of the graph as well as the latency of access to the graph nodes and neighbors. We implemented graph aware caching on a distributed data store Apache HBase in the Hadoop family. Performance evaluations showed up to 15x speedup on the benchmark datasets preferring our new graph aware policy over non-aware policies. We also show how to improve the performance of existing caching algorithms for distributed graphs by exploiting the topology information. © 2015 IEEE.