Tributaries and deltas: Efficient and robust aggregation in sensor network streams

Existing energy-efficient approaches to in-network aggregation in sensor networks can be classified into two categories, tree-based and multi-path-based, with each having unique strengths and weaknesses. In this paper, we introduce Tributary-Delta, a novel approach that combines the advantages of the tree and multi-path approaches by running them simultaneously in different regions of the network. We present schemes for adjusting the regions in response to changes in network conditions, and show how many useful aggregates can be readily computed within this new framework. We then show how a difficult aggregate for this context— finding frequent items—can be efficiently computed within the framework. To this end, we devise the first algorithm for frequent items (and for quantiles) that provably minimizes the worst case total communication for non-regular trees. In addition, we give a multi-path algorithm for frequent items that is considerably more accurate than previous approaches. These algorithms form the basis for our efficient Tributary-Delta frequent items algorithm. Through extensive simulation with real-world and synthetic data, we show the significant advantages of our techniques. For example, in computing Count under realistic loss rates, our techniques reduce answer error by up to a factor of 3 compared to any previous technique. 1

Implicit Decomposition for Write-Efficient Connectivity Algorithms

The future of main memory appears to lie in the direction of new technologies that provide strong capacity-to-performance ratios, but have write operations that are much more expensive than reads in terms of latency, bandwidth, and energy. Motivated by this trend, we propose sequential and parallel algorithms to solve graph connectivity problems using significantly fewer writes than conventional algorithms. Our primary algorithmic tool is the construction of an $o(n)$-sized "implicit decomposition" of a bounded-degree graph $G$ on $n$ nodes, which combined with read-only access to $G$ enables fast answers to connectivity and biconnectivity queries on $G$. The construction breaks the linear-write "barrier", resulting in costs that are asymptotically lower than conventional algorithms while adding only a modest cost to querying time. For general non-sparse graphs on $m$ edges, we also provide the first $o(m)$ writes and $O(m)$ operations parallel algorithms for connectivity and biconnectivity. These algorithms provide insight into how applications can efficiently process computations on large graphs in systems with read-write asymmetry

Accounting for Memory Bank Contention and Delay in High-Bandwidth Multiprocessors

This paper considers issues of memory performance in shared memory multiprocessors that provide a high-bandwidth network and in which the memory banks are slower than the processors. We are concerned with the effects of memory bank contention, memory bank delay, and the bank expansion factor (the ratio of number of banks to number of processors) on performance, particularly for irregular memory access patterns. This work was motivated by observed discrepancies between predicted and actual performance in a number of irregular algorithms implemented for the cray C90 when the memory contention at a particular location is high. We develop a formal framework for studying memory bank contention and delay, and show several results, both experimental and theoretical. We first show experimentally that our framework is a good predictor of performance on the cray C90 and J90, providing a good accounting of bank contention and delay. Second, we show that it often improves performance to have addi..