From Maxout to Channel-Out: Encoding Information on Sparse Pathways

Motivated by an important insight from neural science, we propose a new framework for understanding the success of the recently proposed "maxout" networks. The framework is based on encoding information on sparse pathways and recognizing the correct pathway at inference time. Elaborating further on this insight, we propose a novel deep network architecture, called "channel-out" network, which takes a much better advantage of sparse pathway encoding. In channel-out networks, pathways are not only formed a posteriori, but they are also actively selected according to the inference outputs from the lower layers. From a mathematical perspective, channel-out networks can represent a wider class of piece-wise continuous functions, thereby endowing the network with more expressive power than that of maxout networks. We test our channel-out networks on several well-known image classification benchmarks, setting new state-of-the-art performance on CIFAR-100 and STL-10, which represent some of the "harder" image classification benchmarks.Comment: 10 pages including the appendix, 9 figure

On the Difficulty of Manhattan Channel Routing

We show that channel routing in the Manhattan model remains difficult even when all nets are single-sided. Given a set of n single-sided nets, we consider the problem of determining the minimum number of tracks required to obtain a dogleg-free routing. In addition to showing that the decision version of the problem isNP-complete, we show that there are problems requiring at least d+Omega(sqrt(n)) tracks, where d is the density. This existential lower bound does not follow from any of the known lower bounds in the literature

Constructing Inverted Files: To MapReduce or Not Revisited

Current high-throughput algorithms for constructing inverted files all follow the MapReduce framework, which presents a high-level programming model that hides the complexities of parallel programming. In this paper, we take an alternative approach and develop a novel strategy that exploits the current and emerging architectures of multicore processors. Our algorithm is based on a high-throughput pipelined strategy that produces parallel parsed streams, which are immediately consumed at the same rate by parallel indexers. We have performed extensive tests of our algorithm on a cluster of 32 nodes, and were able to achieve a throughput close to the peak throughput of the I/O system: a throughput of 280 MB/s on a single node and a throughput that ranges between 5.15 GB/s (1 Gb/s Ethernet interconnect) and 6.12GB/s (10Gb/s InfiniBand interconnect) on a cluster with 32 nodes for processing the ClueWeb09 dataset. Such a performance represents a substantial gain over the best known MapReduce algorithms even when comparing the single node performance of our algorithm to MapReduce algorithms running on large clusters. Our results shed a light on the extent of the performance cost that may be incurred by using the simpler, higher-level MapReduce programming model for large scale applications

Optimization of Linked List Prefix Computations on Multithreaded GPUs Using CUDA

We present a number of optimization techniques to compute prefix sums on linked lists and implement them on multithreaded GPUs using CUDA. Prefix computations on linked structures involve in general highly irregular fine grain memory accesses that are typical of many computations on linked lists, trees, and graphs. While the current generation of GPUs provides substantial computational power and extremely high bandwidth memory accesses, they may appear at first to be primarily geared toward streamed, highly data parallel computations. In this paper, we introduce an optimized multithreaded GPU algorithm for prefix computations through a randomization process that reduces the problem to a large number of fine-grain computations. We map these fine-grain computations onto multithreaded GPUs in such a way that the processing cost per element is shown to be close to the best possible. Our experimental results show scalability for list sizes ranging from 1M nodes to 256M nodes, and significantly improve on the recently published parallel implementations of list ranking, including implementations on the Cell Processor, the MTA-8, and the NVIDIA GeForce 200 series. They also compare favorably to the performance of the best known CUDA algorithm for the scan operation on the Tesla C1060

Archiving Temporal Web Information: Organization of Web Contents for Fast Access and Compact Storage

We address the problem of archiving dynamic web contents over significant time spans. Current schemes crawl the web contents at regular time intervals and archive the contents after each crawl regardless of whether or not the contents have changed between consecutive crawls. Our goal is to store newly crawled web contents only when they are different than the previous crawl, while ensuring accurate and quick retrieval of archived contents based on arbitrary temporal queries over the archived time period. In this paper, we develop a scheme that stores unique temporal web contents in containers following the widely used ARC/WARC format, and that provides quick access to the archived contents for arbitrary temporal queries. A novel component of our scheme is the use of a new indexing structure based on the concept of persistent or multi-version data structures. Our scheme can be shown to be asymptotically optimal both in storage utilization and insert/retrieval time. We illustrate the performance of our method on two very different data sets from the Stanford WebBase project, the first reflecting very dynamic web contents and the second relatively static web contents. The experimental results clearly illustrate the substantial storage savings achieved by eliminating duplicate contents detected between consecutive crawls, as well as the speed at which our method can find the archived contents specified through arbitrary temporal queries

Web Archiving: Organizing Web Objects into Web Containers to Optimize Access

The web is becoming the preferred medium for communicating and storing information pertaining to almost any human activity. However it is an ephemeral medium whose contents are constantly changing, resulting in a permanent loss of part of our cultural and scientific heritage on a regular basis. Archiving important web contents is a very challenging technical problem due to its tremendous scale and complex structure, extremely dynamic nature, and its rich heterogeneous and deep contents. In this paper, we consider the problem of archiving a linked set of web objects into web containers in such a way as to minimize the number of containers accessed during a typical browsing session. We develop a method that makes use of the notion of PageRank and optimized graph partitioning to enable faster browsing of archived web contents. We include simulation results that illustrate the performance of our scheme and compare it to the common scheme currently used to organize web objects into web containers