10217893
Adaptive Compiler Directed Prefetching for EPIC Processors
The widely acknowledged performance gap between processors and memory has been the subject of much research. In the Explicitly Parallel Instruction Computing (EPIC) paradigm, the combination of in-order issue and the presence of a large number of parallel functional units exacerbate the problem. Prefetching, by hardware, software, or a combination of both, is one of the primary mechanisms advocated to alleviate this problem. In this paper, we propose a new software-based data prefetching mechanism that is the Adaptive Markovian Predictor ( AMP). AMP is suitable for implementation in EPIC processors without significant hardware overhead. Specifically, we introduce a predicated prefetch operation which leverages the concept of an informing load to dynamically adapt to runtime memory behaviors. Furthermore, we employ predicated prefetching in a new optimization framework that also consists of data remapping and off-line learning of Markovian predictors. This distinguishes our approach from early software prefetching techniques that only involve static program analysis. Our experiments show that the proposed framework can effectively remove 10%-30% of the stall cycles due to cache misses for benchmarks from the well-known SPEC and OLDEN suites. The results also show that the framework performs better than pure stride predictors and has lower bandwidth and instruction overheads.
3492450
A framework for data prefetching using off-line training of Markovian predictors
An important technique for alleviating the memory bottleneck is data prefetching. Data prefetching solutions ranging from pure software approach by inserting prefetch instructions through program analysis to purely hardware mechanisms have been proposed. The degrees of success of those techniques are dependent on the nature of the applications. The need for innovative approach is rapidly growing with the introduction of applications such as object-oriented applications that show dynamically changing memory access behavior In this paper, we propose a novel framework for the use of data prefetchers that are trained off-line using smart learning algorithms to produce prediction models which captures hidden memory access patterns. Once built, those prediction models are loaded into a data prefetching unit in the CPU at the appropriate point during the runtime to drive the prefetching. On average by using table size of about 8KB size, we were able to achieve prediction accuracy of about 68% through our own proposed learning method and performance was boosted about 37% on average on the benchmarks we tested. Furthermore, we believe our proposed framework is amenable to other predictors and can be done as a phase of the profiling-optimizing-compiler.
3103350
Random Matchings in Regular Graphs
d-regular graph G, let M be chosen uniformly at random from the set of all matchings of G, and for let be the probability that M does not cover x.We show that for large d, the 's and the mean μ and variance of are determined to within small tolerances just by d and (in the case of μ and ) :
Theorem. For any d-regular graph G,
(a), so that ,
(b),where the rates of convergence depend only on d.
6065142
On effective data clustering in bitemporal databases
Temporal databases provide built-in supports for efficient recording and querying of time-evolving data. In this paper, data clustering issues in temporal database environment are addressed. Data clustering is one of the most effective techniques that can improve performance of a database system. However, data clustering methods for conventional databases do not perform well in temporal databases because there exist crucial differences between their query patterns. We propose a data clustering measure, called Temporal Affinity, that can be used for the clustering of temporal data. The temporal affinity, which is based on the analysis of query patterns in temporal databases, reflects the closeness of temporal data objects in viewpoints of temporal query processing. We perform experiments to evaluate the proposed measure. The experimental results show that a data clustering method with the temporal affinity works better than other methods.
13912277
The Use of Speech Retrieval Systems: A Study Design
What criteria for relevance do users apply when selecting a speech recording? What attributes of the recording do they rely on for each criterion? This paper proposes a qualitative research study design to explore those questions. A conceptual framework is presented, research questions are introduced, and the study design is described. The paper concludes with some observations on how the results of the study might inform the design of future systems.
12500430
Energy-efficient file placement techniques for heterogeneous mobile storage systems
While hard disk drives are the most common secondary storage devices, their high power consumption and low shock-resistance limit them as an ideal mobile storage solution. On the other hand, flash memory devices overcome the main problems of hard disk drives, but they are still more expensive in the cost per bit over hard disk drives and can only support a limited number of erase cycles. In this paper, we show that combining the merits of a hard disk and a flash memory device can produce an energy-efficient secondary storage solution for mobile platforms. We propose an energy-efficient file placement technique for such heterogeneous storage systems. The proposed technique adapts an existing data concentration technique by separating read and write I/O requests. Experimental results show that the proposed technique reduces the energy consumption by up to 74.5% when the combination of a 1.8" disk and a flash memory is used instead of a single 2.5" disk, at the cost of small increase in the average response time.
14673300
A Polynomial Time Approximation Scheme for the Problem of Interconnecting Highways
AbstractThe objective of the Interconnecting Highways problem is to construct roads of minimum total length to interconnect n given highways under the constraint that the roads can intersect each highway only at one point in a designated interval which is a line segment. We present a polynomial time approximation scheme for this problem by applying Arora's framework (Arora, 1998; also available from http:www.cs.princeton.edu/~arora). For every fixed c > 1 and given any n line segments in the plane, a randomized version of the scheme finds a 
$$\left( {1 + \frac{1}{c}} \right)$$
-approximation to the optimal cost in O(nO(c)log(n) time.