255,103 research outputs found
Indirect addressing and load balancing for faster solution to Mandelbrot Set on SIMD architectures
SIMD computers with local indirect addressing allow programs to have queues and buffers, making certain kinds of problems much more efficient. Examined here are a class of problems characterized by computations on data points where the computation is identical, but the convergence rate is data dependent. Normally, in this situation, the algorithm time is governed by the maximum number of iterations required by each point. Using indirect addressing allows a processor to proceed to the next data point when it is done, reducing the overall number of iterations required to approach the mean convergence rate when a sufficiently large problem set is solved. Load balancing techniques can be applied for additional performance improvement. Simulations of this technique applied to solving Mandelbrot Sets indicate significant performance gains
Method of up-front load balancing for local memory parallel processors
In a parallel processing computer system with multiple processing units and shared memory, a method is disclosed for uniformly balancing the aggregate computational load in, and utilizing minimal memory by, a network having identical computations to be executed at each connection therein. Read-only and read-write memory are subdivided into a plurality of process sets, which function like artificial processing units. Said plurality of process sets is iteratively merged and reduced to the number of processing units without exceeding the balance load. Said merger is based upon the value of a partition threshold, which is a measure of the memory utilization. The turnaround time and memory savings of the instant method are functions of the number of processing units available and the number of partitions into which the memory is subdivided. Typical results of the preferred embodiment yielded memory savings of from sixty to seventy five percent
Fast Discrete Consensus Based on Gossip for Makespan Minimization in Networked Systems
In this paper we propose a novel algorithm to solve the discrete consensus problem, i.e., the problem of distributing evenly a set of tokens of arbitrary weight among the nodes of a networked system. Tokens are tasks to be executed by the nodes and the proposed distributed algorithm minimizes monotonically the makespan of the assigned tasks. The algorithm is based on gossip-like asynchronous local interactions between the nodes. The convergence time of the proposed algorithm is superior with respect to the state of the art of discrete and quantized consensus by at least a factor O(n) in both theoretical and empirical comparisons
Multilevel MDA-Lite Paris Traceroute
Since its introduction in 2006-2007, Paris Traceroute and its Multipath
Detection Algorithm (MDA) have been used to conduct well over a billion IP
level multipath route traces from platforms such as M-Lab. Unfortunately, the
MDA requires a large number of packets in order to trace an entire topology of
load balanced paths between a source and a destination, which makes it
undesirable for platforms that otherwise deploy Paris Traceroute, such as RIPE
Atlas. In this paper we present a major update to the Paris Traceroute tool.
Our contributions are: (1) MDA-Lite, an alternative to the MDA that
significantly cuts overhead while maintaining a low failure probability; (2)
Fakeroute, a simulator that enables validation of a multipath route tracing
tool's adherence to its claimed failure probability bounds; (3) multilevel
multipath route tracing, with, for the first time, a Traceroute tool that
provides a router-level view of multipath routes; and (4) surveys at both the
IP and router levels of multipath routing in the Internet, showing, among other
things, that load balancing topologies have increased in size well beyond what
has been previously reported as recently as 2016. The data and the software
underlying these results are publicly available.Comment: Preprint. To appear in Proc. ACM Internet Measurement Conference 201
Integrative Dynamic Reconfiguration in a Parallel Stream Processing Engine
Load balancing, operator instance collocations and horizontal scaling are
critical issues in Parallel Stream Processing Engines to achieve low data
processing latency, optimized cluster utilization and minimized communication
cost respectively. In previous work, these issues are typically tackled
separately and independently. We argue that these problems are tightly coupled
in the sense that they all need to determine the allocations of workloads and
migrate computational states at runtime. Optimizing them independently would
result in suboptimal solutions. Therefore, in this paper, we investigate how
these three issues can be modeled as one integrated optimization problem. In
particular, we first consider jobs where workload allocations have little
effect on the communication cost, and model the problem of load balance as a
Mixed-Integer Linear Program. Afterwards, we present an extended solution
called ALBIC, which support general jobs. We implement the proposed techniques
on top of Apache Storm, an open-source Parallel Stream Processing Engine. The
extensive experimental results over both synthetic and real datasets show that
our techniques clearly outperform existing approaches
Comparison of Balancing Techniques for Multimedia IR over Imbalanced Datasets
A promising method to improve the performance of information retrieval systems is to approach retrieval tasks as a supervised classification problem. Previous user interactions, e.g. gathered from a thorough log file analysis, can be used to train classifiers which aim to inference relevance of retrieved documents based on user interactions. A problem in this approach is, however, the large imbalance ratio between relevant and non-relevant documents in the collection. In standard test collection as used in academic evaluation frameworks such as TREC, non-relevant documents outnumber relevant documents by far. In this work, we address this imbalance problem in the multimedia domain. We focus on the logs of two multimedia user studies which are highly imbalanced. We compare a naiinodotve solution of randomly deleting documents belonging to the majority class with various balancing algorithms coming from different fields: data classification and text classification. Our experiments indicate that all algorithms improve the classification performance of just deleting at random from the dominant class
Balanced Combinations of Solutions in Multi-Objective Optimization
For every list of integers x_1, ..., x_m there is some j such that x_1 + ...
+ x_j - x_{j+1} - ... - x_m \approx 0. So the list can be nearly balanced and
for this we only need one alternation between addition and subtraction. But
what if the x_i are k-dimensional integer vectors? Using results from
topological degree theory we show that balancing is still possible, now with k
alternations.
This result is useful in multi-objective optimization, as it allows a
polynomial-time computable balance of two alternatives with conflicting costs.
The application to two multi-objective optimization problems yields the
following results:
- A randomized 1/2-approximation for multi-objective maximum asymmetric
traveling salesman, which improves and simplifies the best known approximation
for this problem.
- A deterministic 1/2-approximation for multi-objective maximum weighted
satisfiability
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