AsterixDB is a new, full-function BDMS (Big Data Management System) with a feature set that distinguishes it from other platforms in today's open source Big Data ecosystem. Its features make it well-suited to applications like web data warehousing, social data storage and analysis, and other use cases related to Big Data. AsterixDB has a flexible NoSQL style data model; a query language that supports a wide range of queries; a scalable runtime; partitioned, LSM-based data storage and indexing (including B + -tree, R-tree, and text indexes); support for external as well as natively stored data; a rich set of built-in types; support for fuzzy, spatial, and temporal types and queries; a built-in notion of data feeds for ingestion of data; and transaction support akin to that of a NoSQL store. Development of AsterixDB began in 2009 and led to a mid-2013 initial open source release. This paper is the first complete description of the resulting open source AsterixDB system. Covered herein are the system's data model, its query language, and its software architecture. Also included are a summary of the current status of the project and a first glimpse into how AsterixDB performs when compared to alternative technologies, including a parallel relational DBMS, a popular NoSQL store, and a popular Hadoop-based SQL data analytics platform, for things that both technologies can do. Also included is a brief description of some initial trials that the system has undergone and the lessons learned (and plans laid) based on those early "customer" engagements.

In this paper, a novel genetic algorithm based on immunity (GABI) on the basis of parallel genetic algorithms (PGA) is proposed in order to overcome some defects of them, such as premature and slow convergence rate. The global performance of the algorithm is improved by introducing immunity theory into PGA. This is revealed in the following two aspects. One is that the immune selection based on proposed adjustable geometric-progression rank-based selection can prevent the algorithm from premature. The other is that convergence rate can be accelerate by individual migration strategy between subpopulations based on immune memory mechanism. In this algorithm, the idea of multiple subpopulations evolution based on improved adaptive crossover and mutation is adopted. To be hybridized with the Powell method can further improve local searching performance of the algorithm. An example of layout design shows that GABI is feasible and effective.

Multi-cores are becoming ubiquitous as exemplified by Sun's Niagra-2, Intel's Nehalem and AMD's Sau Paulo octal cores. The number of cores per chip is expected to rise in foreseeable future, as evidenced by the recently announced Intel's 80-core Teraflops Research Chip. Exploiting the parallelism of multicores necessitates concurrent software. One way to parallelize programs, not amenable to auto-parallelization, is via explicit synchronization. The placement of the synchronization primitives has a large bearing on how much thread-level parallelism (TLP) can be achieved. In this paper, we propose novel predication-based and other adjunct synchronization optimizations which facilitate exploitation on higher level of TLP than what can be achieved using the state-of-the-art. We demonstrate the efficacy of our techniques, on a real machine, using real codes, specifically, from the industry-standard SPEC CPU benchmarks and other widely used open source codes such as PostgreSQL. Our results show that the proposed techniques yield significantly higher levels of TLP than the state-of-the-art.

Harnessing the hardware parallelism of the emerging multi-cores systems necessitates concurrent software. Unfortunately, most of the existing mainstream software is sequential in nature. Although one could auto-parallelize a given program, the efficacy of this is largely limited to floating-point codes. One of the ways to alleviate the above limitation is to parallelize programs, which cannot be auto-parallelized, via explicit synchronization. In this regard, efficient placement of the synchronization primitives - say, post, wait - plays a key role in achieving high degree of thread-level parallelism (TLP). In this paper, we propose novel compiler techniques for the above. Specifically, given a control flow graph (CFG), the proposed techniques place a post as early as possible and place a wait as late as possible in the CFG, subject to dependences. We demonstrate the efficacy of our techniques, on a real machine, using real codes, specifically, from the industry-standard SPEC CPU benchmarks, the Linux kernel and other widely used open source codes. Our results show that the proposed techniques yield significantly higher levels of TLP than the state-of-the-art.