41230839
FPGA-based reconfigurable computing II
9645285
Using scratchpad to exploit object locality in Java
Performance of modern computers is tied closely to the effective use of cache because of the continually increasing speed discrepancy between processors and main memory. We demonstrate that generational garbage collection employed by a system with cache and scratchpad memory can take advantage of the locality of small short-lived objects in Java and reduce memory traffic by as much as 20% when compared to a cache-only configuration. Converting half of the cache to scratchpad can be more effective at reducing memory traffic than doubling or even quadrupling the size of the cache for several of the applications in SPECjvm98.
1021056
A performance perspective on the Active Memory System
Abstract One major problem of using Java in real-time and embedded devices is the non-deterministic turnaround time of dynamic memory management systems (memory allocation and garbage collection). For the allocation, the non-determinism is often contributed by the time to perform searching, splitting, and coalescing. For the garbage collection, the turnaround time is usually determined by the size of the heap, the number of live objects, the number of object collected, and the amount of garbage collected. Even with the current state-of-the-art garbage collectors (generational and incremental schemes), they may or may not guarantee the worst-case latency. Moreover, such schemes often prolong overall garbage collection time. In this paper, the performance analysis of the proposed Active Memory Module (AMM) for embedded systems is presented. Unlike the software counterparts, the AMM can perform a memory allocation in a predictable and bounded fashion (14 cycles). Moreover, it can also yield a bounded sweeping time regardless of the number of live objects or heap size. By utilizing the proposed system, the overall speedup can be as high as 23% compared to the garbage collection system of the JDK 1.2.2 running in classic mode.
18001825
Architectural support for dynamic memory management
Recent advances in software engineering, such as graphical user interfaces and object-oriented programming, have caused applications to become more memory intensive. These applications tend to allocate dynamic memory prolifically. Moreover, automatic dynamic memory reclamation (garbage collection, GC) has become a popular feature in modern programming languages. As a result, the time consumed by dynamic storage management can be up to one-third of the program execution time. This illustrates the need for a high-performance memory management scheme. This paper presents a top-level design and evaluation of the proposed instruction extensions to facilitate heap management.
12198410
Scalable hardware-algorithm for mark-sweep garbage collection
The memory-intensive nature of object-oriented languages such as C++ and Java has created the need for high-performance dynamic memory management. Object-oriented applications often generate higher memory intensity in the heap region. Thus, a high-performance memory manager is needed to cope with such applications. As today's VLSI technology advances, it becomes increasingly attractive to map software algorithms such as malloc(), free() and garbage collection into hardware. This paper presents a hardware design of a sweeping function (for mark-and-sweep garbage collection) that fully utilizes the advantages of combinational logic. In our scheme, the bit sweep can detect and sweep the garbage in a constant time. Bit-map marking in software can improve the cache performance and reduce number of page faults; however, it often requires several instructions to perform a single mark. In our scheme, only one hardware instruction is required per mark. Moreover, since the complexity of the sweeping phase is often higher than the marking phase, the garbage collection time may be substantially improved. The hardware complexity of the proposed scheme (bit-sweeper) is O(n), where n represents the size of the bit map.
39527990
FPGA-based reconfigurable computing
17161223
Speculative incoherent cache protocols
Multiprocessing and multithreading are becoming ubiquitous even on single chips. With increasing cache sizes, coherence misses in such systems will account for a larger fraction of all cache misses. As communication latencies increase, this larger fraction of coherence misses will cause significant and increased performance losses. Tuning coherence protocols for specific communication patterns and applications can reduce communication latencies. However, these optimizations increase a protocol's design complexity, making the protocol difficult to verify. A competing approach requires parallel programmers to tune applications to work well with simpler protocols. Speculative execution has successfully improved performance in various scenarios. We propose a new type of load speculation, called coherence decoupling. Coherence decoupling is a microarchitectural mechanism that implements separate protocols for speculative use and for the eventual verification of values. The technique reduces the effect of long communication latencies while mitigating the burdens on the coherence protocol designer and the parallel programmer
37599184
Finding frequent itemsets over online data streams
Abstract Conventional data mining methods for finding frequent itemsets require considerable computing time to produce their results from a large data set. Due to this reason, it is almost impossible to apply them to an analysis task in an online data stream where a new transaction is continuously generated at a rapid rate. An algorithm for finding frequent itemsets over an online data stream should support flexible trade-off between processing time and mining accuracy. Furthermore, the most up-to-date resulting set of frequent itemsets should be available quickly at any moment. To satisfy these requirements, this paper proposes a data mining method for finding frequent itemsets over an online data stream. The proposed method examines each transaction one-by-one without any candidate generation process. The count of an itemset that appears in each transaction is monitored by a lexicographic tree resided in main memory. The current set of monitored itemsets in an online data stream is minimized by two major operations: delayed-insertion and pruning . The former is delaying the insertion of a new itemset in recent transactions until the itemset becomes significant enough to be monitored. The latter is pruning a monitored itemset when the itemset turns out to be insignificant. The number of monitored itemsets can be flexibly controlled by the thresholds of these two operations. As the number of monitored itemsets is decreased, frequent itemsets in the online data stream are more rapidly traced while they are less accurate. The performance of the proposed method is analyzed through a series of experiments in order to identify its various characteristics.
27312959
Sorting a sequence of strong kings in a tournament
A king in a tournament is a player who beats any other player directly or indirectly. According to the existence of a king in every tournament, Wu and Sheng [Inform. Process. Lett. 79 (2001) 297-299] recently presented an algorithm for finding a sorted sequence of kings in a tournament of size n, i.e., a sequence of players u1, u2,....,un such that ui → ui+1 (ui beats ui+1) and ui is a king in the sub-tournament induced by {ui, ui+1,.....,un} for each i = 1, 2,....,n - 1. With each pair u,v of players in a tournament, let b(u,v) denote the number of third players used for u to beat v indirectly. Then, a king u is called a strong king if the following condition is fulfilled: if v → u then b(u, v) > b(v,u). In the sequel, we will show that the algorithm proposed by Wu and Sheng indeed generates a sorted sequence of strong kings, which is more restricted than the previous one.
9345376
Finding recently frequent itemsets adaptively over online transactional data streams,
A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Consequently, the knowledge embedded in a data stream is more likely to be changed as time goes by. Identifying the recent change of a data stream, especially for an online data stream, can provide valuable information for the analysis of the data stream. However, most of mining algorithms or frequency approximation algorithms over a data stream do not differentiate the information of recently generated data elements from the obsolete information of old data elements which may be no longer useful or possibly invalid at present. Therefore, they are not able to extract the recent change of information in a data stream adaptively. This paper proposes a data mining method for finding recently frequent itemsets adaptively over an online transactional data stream. The effect of old transactions on the current mining result of a data steam is diminished by decaying the old occurrences of each itemset as time goes by. Furthermore, several optimization techniques are devised to minimize processing time as well as memory usage. Finally, the performance of the proposed method is analyzed by a series of experiments to identify its various characteristics.
4458929
Including Biological Literature Improves Homology Search
Annotating the tremendous amount of sequence information being generated requires accurate automated methods for recognizing homology. Although sequence similarity is only one of many indicators of evolutionary homology, it is often the only one used. Here we find that supplementing sequence similarity with information from biomedical literature is successful in increasing the accuracy of homology search results. We modified the PSI-BLAST algorithm to use literature similarity in each iteration of its database search. The modified algorithm is evaluated and compared to standard PSI-BLAST in searching for homologous proteins. The performance of the modified algorithm achieved 32% recall with 95% precision, while the original one achieved 33% recall with 84% precision; the literature similarity requirement preserved the sensitive characteristic of the PSI-BLAST algorithm while improving the precision.