A bridging model for parallel computation
Communications of the ACM
Multilevel k-way partitioning scheme for irregular graphs
Journal of Parallel and Distributed Computing
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
The connectivity server: fast access to linkage information on the Web
WWW7 Proceedings of the seventh international conference on World Wide Web 7
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Transactional information systems: theory, algorithms, and the practice of concurrency control and recovery
Clustering Techniques for Minimizing External Path Length
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Spectral partitioning works: planar graphs and finite element meshes
FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
Lifting sequential graph algorithms for distributed-memory parallel computation
OOPSLA '05 Proceedings of the 20th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Evaluating MapReduce for Multi-core and Multiprocessor Systems
HPCA '07 Proceedings of the 2007 IEEE 13th International Symposium on High Performance Computer Architecture
Proceedings of the 20th ACM conference on Hypertext and hypermedia
Pregel: a system for large-scale graph processing
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
The little engine(s) that could: scaling online social networks
Proceedings of the ACM SIGCOMM 2010 conference
OSDI'08 Proceedings of the 8th USENIX conference on Operating systems design and implementation
Scalable Graph Exploration on Multicore Processors
Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis
Crunching large graphs with commodity processors
HotPar'11 Proceedings of the 3rd USENIX conference on Hot topic in parallelism
Optimizing Large-Scale Graph Analysis on a Multi-threaded, Multi-core Platform
IPDPS '11 Proceedings of the 2011 IEEE International Parallel & Distributed Processing Symposium
Efficient Parallel Graph Exploration on Multi-Core CPU and GPU
PACT '11 Proceedings of the 2011 International Conference on Parallel Architectures and Compilation Techniques
Cache craftiness for fast multicore key-value storage
Proceedings of the 7th ACM european conference on Computer Systems
Acolyte: an in-memory social network query system
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
Ligra: a lightweight graph processing framework for shared memory
Proceedings of the 18th ACM SIGPLAN symposium on Principles and practice of parallel programming
Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles
ACM SIGOPS 24th Symposium on Operating Systems Principles
X-Stream: edge-centric graph processing using streaming partitions
Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles
FENNEL: streaming graph partitioning for massive scale graphs
Proceedings of the 7th ACM international conference on Web search and data mining
Proceedings of the VLDB Endowment
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Grace is a graph-aware, in-memory, transactional graph management system, specifically built for real-time queries and fast iterative computations. It is designed to run on large multi-cores, taking advantage of the inherent parallelism to improve its performance. Grace contains a number of graph-specific and multi-core-specific optimizations including graph partitioning, careful in-memory vertex ordering, updates batching, and load-balancing. It supports queries, searches, iterative computations, and transactional updates. Grace scales to large graphs (e.g., a Hotmail graph with 320 million vertices) and performs up to two orders of magnitude faster than commercial key-value stores and graph databases.