A systematic transcriptome survey is essential for the characterization and comprehension of the molecular basis underlying phenotypic variations. Recently developed RNA-seq methodology has facilitated efficient data acquisition and information mining of transcriptomes in multiple tissues/cell lines. Current mammalian transcriptomic databases are either tissue-specific or species-specific, and they lack in-depth comparative features across tissues and species. Here, we present a mammalian transcriptomic database (MTD) that is focused on mammalian transcriptomes, and the current version contains data from humans, mice, rats and pigs. Regarding the core features, the MTD browses genes based on their neighboring genomic coordinates or joint KEGG pathway and provides expression information on exons, transcripts and genes by integrating them into a genome browser. We developed a novel nomenclature for each transcript that considers its genomic position and transcriptional features. The MTD allows a flexible search of genes or isoforms with user-defined transcriptional characteristics and provides both table-based descriptions and associated visualizations. To elucidate the dynamics of gene expression regulation, the MTD also enables comparative transcriptomic analysis in both intraspecies and interspecies manner. The MTD thus constitutes a valuable resource for transcriptomic and evolutionary studies. The MTD is freely accessible at http://mtd.cbi.ac.cn.

In multithreaded programming, locks are frequently used as mechanism for synchronization. Because today's operating systems do not consider lock usage as scheduling criterion, scheduling decisions can be unfavorable to multithreaded applications, leading to performance issues such as convoying and heavy lock contention in systems with multiple processors. Previous efforts to address these issues (e.g., transactional memory, lock-free data structure) often treat scheduling decisions as a fact of life, and therefore these solutions try to cope with the consequences of undesirable scheduling instead of dealing with the problem directly. In this paper, we introduce Contention-Aware Scheduler (CA-Scheduler), which is designed to support efficient execution of large multithreaded Java applications in multiprocessor systems. Our proposed scheduler employs scheduling policy that reduces lock contention. As will be shown in this paper, our prototype implementation of the CA-Scheduler in Linux and Sun HotSpot virtual machine only incurs 3.5% runtime overhead, while the overall performance differences, when compared with system with no contention awareness, range from degradation of 3% in small multithreaded benchmark to an improvement of 15% in large Java application server benchmark.

An efficient and distributed scheme for file mapping or file lookup is critical in decentralizing metadata management within a group of metadata servers. This paper presents a novel technique called Hierarchical Bloom Filter Arrays (HBA) to map filenames to the metadata servers holding their metadata. Two levels of probabilistic arrays, namely, the Bloom filter arrays with different levels of accuracies, are used on each metadata server. One array, with lower accuracy and representing the distribution of the entire metadata, trades accuracy for significantly reduced memory overhead, whereas the other array, with higher accuracy, caches partial distribution information and exploits the temporal locality of file access patterns. Both arrays are replicated to all metadata servers to support fast local lookups. We evaluate HBA through extensive trace-driven simulations and implementation in Linux. Simulation results show our HBA design to be highly effective and efficient in improving the performance and scalability of file systems in clusters with 1,000 to 10,000 nodes (or superclusters) and with the amount of data in the petabyte scale or higher. Our implementation indicates that HBA can reduce the metadata operation time of a single-metadata-server architecture by a factor of up to 43.9 when the system is configured with 16 metadata servers.

Past studies have shown that objects are created and then die in phases. Thus, one way to sustain good garbage collection efficiency is to have a large enough heap to allow many allocation phases to complete and most of the objects to die before invoking garbage collection. However, such an operating environment is hard to maintain in large multithreaded applications because most typical time-sharing schedulers are not allocation-phase cognizant; i.e., they often schedule threads in a way that prevents them from completing their allocation phases quickly. Thus, when garbage collection is invoked, most allocation phases have yet to be completed, resulting in poor collection efficiency. We introduce two new scheduling strategies, LARF (lower allocation rate first) and MQRR (memory-quantum round robin) designed to be allocation-phase aware by assigning higher execution priority to threads in computation-oriented phases. The simulation results show thatallthe reductions of the garbage collection time in a generational collector can range from 0%-27% when compare to a round robin scheduler. The reductions of the overall execution time and the average thread turnaround time range from -0.1%-3% and -0.1%-13%, respectively.

To date, the most commonly used criterion for invoking garbage collection (GC) is based on heap usage; that is, garbage collection is invoked when the heap or an area inside the heap is full. This approach can suffer from two performance shortcomings: untimely garbage collection invocations and large volumes of surviving objects. In this work, we explore a new GC triggering approach called MicroPhase that exploits two observations: (i) allocation requests occur in phases and (ii) phase boundaries coincide with times when most objects also die. Thus, proactively invoking garbage collection at these phase boundaries can yield high efficiency. We extended the HotSpot virtual machine from Sun Microsystems to support MicroPhase and conducted experiments using 20 benchmarks. The experimental results indicate that our technique can reduce the GC times in 19 applications. The differences in GC overhead range from an increase of 1% to a decrease of 26% when the heap is set to twice the maximum live-size. As a result, MicroPhase can improve the overall performance of 13 benchmarks. The performance differences range from a degradation of 2.5% to an improvement of 14%.

Reference counting is an incremental garbage collection technique that yields nearly unnoticeable pause time but can suffer from high processing overhead. Previous attempts to use hardware to reduce this overhead have shown successes but limited applicability. With recent discovery that reference counting can be well suited for Java embedded devices, it is worthwhile to rethink hardware solutions that can further improve its performance by leveraging current trends in embedded computing. In this paper, we introduce a custom instruction solution that is (i) more practical because it only provides hardware support for the expensive but straight-forward software function-reference count update; the existing complex runtime functions such as memory allocation and liberation remain unchanged and (ii) better positioned for widespread adoption because it is designed to leverage readily available configurable logics in many embedded processor cores. As a proof-of-concept, we implement two reference counting algorithms that utilize the proposed custom instructions on Stretch S5000 software reconfigurable processors. We then analyze the performance impacts on the execution time as well as the architectural behavior. The results show that we can achieve as much as 70% performance gain over pure software implementation.

With the development of enterprise Intranet and cluster servers, many emerging security challenges could not be solved by conventional firewall due to its inner deficiency. To address these security problems, we present a mobile-agent based distributed dynamic /spl mu/Firewall architecture. In this architecture, special mobile agents implement a dynamic security policy reconfiguration and enhance the scalability. Each /spl mu/Firewall is built with a packet filter and DTE-enhanced evaluator to provide dual fine-grain protection at the individual host level. A distributed intrusion detection and response (DIDR) system provides a fast response to both external and internal attacks, and allows an adaptive change in the security policy in the protected network. The DIDR system provides the infrastructure to support hierarchical intrusion responses and dynamic security capabilities. The distributed security architecture is scalable, topology independent, and intrusion-tolerant.