Empirical Evaluation of the Parallel Distribution Sweeping Framework on Multicore Architectures

In this paper, we perform an empirical evaluation of the Parallel External Memory (PEM) model in the context of geometric problems. In particular, we implement the parallel distribution sweeping framework of Ajwani, Sitchinava and Zeh to solve batched 1-dimensional stabbing max problem. While modern processors consist of sophisticated memory systems (multiple levels of caches, set associativity, TLB, prefetching), we empirically show that algorithms designed in simple models, that focus on minimizing the I/O transfers between shared memory and single level cache, can lead to efficient software on current multicore architectures. Our implementation exhibits significantly fewer accesses to slow DRAM and, therefore, outperforms traditional approaches based on plane sweep and two-way divide and conquer.Comment: Longer version of ESA'13 pape

Is random access memory random?

Most software is contructed on the assumption that the programs and data are stored in random access memory (RAM). Physical limitations on the relative speeds of processor and memory elements lead to a variety of memory organizations that match processor addressing rate with memory service rate. These include interleaved and cached memory. A very high fraction of a processor's address requests can be satified from the cache without reference to the main memory. The cache requests information from main memory in blocks that can be transferred at the full memory speed. Programmers who organize algorithms for locality can realize the highest performance from these computers

Dynamic Parameter Allocation in Parameter Servers

To keep up with increasing dataset sizes and model complexity, distributed training has become a necessity for large machine learning tasks. Parameter servers ease the implementation of distributed parameter management---a key concern in distributed training---, but can induce severe communication overhead. To reduce communication overhead, distributed machine learning algorithms use techniques to increase parameter access locality (PAL), achieving up to linear speed-ups. We found that existing parameter servers provide only limited support for PAL techniques, however, and therefore prevent efficient training. In this paper, we explore whether and to what extent PAL techniques can be supported, and whether such support is beneficial. We propose to integrate dynamic parameter allocation into parameter servers, describe an efficient implementation of such a parameter server called Lapse, and experimentally compare its performance to existing parameter servers across a number of machine learning tasks. We found that Lapse provides near-linear scaling and can be orders of magnitude faster than existing parameter servers

Parallel simulation of Population Dynamics P systems: updates and roadmap

Population Dynamics P systems are a type of multienvironment P systems that serve as a formal modeling framework for real ecosystems. The accurate simulation of these probabilisticmodels, e.g. with Direct distribution based on Consistent Blocks Algorithm, entails large run times. Hence, parallel platforms such as GPUs have been employed to speedup the simulation. In 2012, the first GPU simulator of PDP systems was presented. However, it was able to run only randomly generated PDP systems. In this paper, we present current updates made on this simulator, involving an input modu le for binary files and an output module for CSV files. Finally, the simulator has been experimentally validated with a real ecosystem model, and its performance has been tested with two high-end GPUs: Tesla C1060 and K40.Ministerio de Economía y Competitividad TIN2012-37434Junta de Andalucía P08-TIC-0420

Time and information in perceptual adaptation to speech

Presubmission manuscript and supplementary files (stimuli, stimulus presentation code, data, data analysis code).Perceptual adaptation to a talker enables listeners to efficiently resolve the many-to-many mapping between variable speech acoustics and abstract linguistic representations. However, models of speech perception have not delved into the variety or the quantity of information necessary for successful adaptation, nor how adaptation unfolds over time. In three experiments using speeded classification of spoken words, we explored how the quantity (duration), quality (phonetic detail), and temporal continuity of talker-specific context contribute to facilitating perceptual adaptation to speech. In single- and mixed-talker conditions, listeners identified phonetically-confusable target words in isolation or preceded by carrier phrases of varying lengths and phonetic content, spoken by the same talker as the target word. Word identification was always slower in mixed-talker conditions than single-talker ones. However, interference from talker variability decreased as the duration of preceding speech increased but was not affected by the amount of preceding talker-specific phonetic information. Furthermore, efficiency gains from adaptation depended on temporal continuity between preceding speech and the target word. These results suggest that perceptual adaptation to speech may be understood via models of auditory streaming, where perceptual continuity of an auditory object (e.g., a talker) facilitates allocation of attentional resources, resulting in more efficient perceptual processing.NIH NIDCD (R03DC014045