4,100 research outputs found
Lessons from the Congested Clique Applied to MapReduce
The main results of this paper are (I) a simulation algorithm which, under
quite general constraints, transforms algorithms running on the Congested
Clique into algorithms running in the MapReduce model, and (II) a distributed
-coloring algorithm running on the Congested Clique which has an
expected running time of (i) rounds, if ;
and (ii) rounds otherwise. Applying the simulation theorem to
the Congested-Clique -coloring algorithm yields an -round
-coloring algorithm in the MapReduce model.
Our simulation algorithm illustrates a natural correspondence between
per-node bandwidth in the Congested Clique model and memory per machine in the
MapReduce model. In the Congested Clique (and more generally, any network in
the model), the major impediment to constructing fast
algorithms is the restriction on message sizes. Similarly, in the
MapReduce model, the combined restrictions on memory per machine and total
system memory have a dominant effect on algorithm design. In showing a fairly
general simulation algorithm, we highlight the similarities and differences
between these models.Comment: 15 page
Observations on Factors Affecting Performance of MapReduce based Apriori on Hadoop Cluster
Designing fast and scalable algorithm for mining frequent itemsets is always
being a most eminent and promising problem of data mining. Apriori is one of
the most broadly used and popular algorithm of frequent itemset mining.
Designing efficient algorithms on MapReduce framework to process and analyze
big datasets is contemporary research nowadays. In this paper, we have focused
on the performance of MapReduce based Apriori on homogeneous as well as on
heterogeneous Hadoop cluster. We have investigated a number of factors that
significantly affects the execution time of MapReduce based Apriori running on
homogeneous and heterogeneous Hadoop Cluster. Factors are specific to both
algorithmic and non-algorithmic improvements. Considered factors specific to
algorithmic improvements are filtered transactions and data structures.
Experimental results show that how an appropriate data structure and filtered
transactions technique drastically reduce the execution time. The
non-algorithmic factors include speculative execution, nodes with poor
performance, data locality & distribution of data blocks, and parallelism
control with input split size. We have applied strategies against these factors
and fine tuned the relevant parameters in our particular application.
Experimental results show that if cluster specific parameters are taken care of
then there is a significant reduction in execution time. Also we have discussed
the issues regarding MapReduce implementation of Apriori which may
significantly influence the performance.Comment: 8 pages, 8 figures, International Conference on Computing,
Communication and Automation (ICCCA2016
Optimizing the MapReduce Framework on Intel Xeon Phi Coprocessor
With the ease-of-programming, flexibility and yet efficiency, MapReduce has
become one of the most popular frameworks for building big-data applications.
MapReduce was originally designed for distributed-computing, and has been
extended to various architectures, e,g, multi-core CPUs, GPUs and FPGAs. In
this work, we focus on optimizing the MapReduce framework on Xeon Phi, which is
the latest product released by Intel based on the Many Integrated Core
Architecture. To the best of our knowledge, this is the first work to optimize
the MapReduce framework on the Xeon Phi.
In our work, we utilize advanced features of the Xeon Phi to achieve high
performance. In order to take advantage of the SIMD vector processing units, we
propose a vectorization friendly technique for the map phase to assist the
auto-vectorization as well as develop SIMD hash computation algorithms.
Furthermore, we utilize MIMD hyper-threading to pipeline the map and reduce to
improve the resource utilization. We also eliminate multiple local arrays but
use low cost atomic operations on the global array for some applications, which
can improve the thread scalability and data locality due to the coherent L2
caches. Finally, for a given application, our framework can either
automatically detect suitable techniques to apply or provide guideline for
users at compilation time. We conduct comprehensive experiments to benchmark
the Xeon Phi and compare our optimized MapReduce framework with a
state-of-the-art multi-core based MapReduce framework (Phoenix++). By
evaluating six real-world applications, the experimental results show that our
optimized framework is 1.2X to 38X faster than Phoenix++ for various
applications on the Xeon Phi
On data skewness, stragglers, and MapReduce progress indicators
We tackle the problem of predicting the performance of MapReduce
applications, designing accurate progress indicators that keep programmers
informed on the percentage of completed computation time during the execution
of a job. Through extensive experiments, we show that state-of-the-art progress
indicators (including the one provided by Hadoop) can be seriously harmed by
data skewness, load unbalancing, and straggling tasks. This is mainly due to
their implicit assumption that the running time depends linearly on the input
size. We thus design a novel profile-guided progress indicator, called
NearestFit, that operates without the linear hypothesis assumption and exploits
a careful combination of nearest neighbor regression and statistical curve
fitting techniques. Our theoretical progress model requires fine-grained
profile data, that can be very difficult to manage in practice. To overcome
this issue, we resort to computing accurate approximations for some of the
quantities used in our model through space- and time-efficient data streaming
algorithms. We implemented NearestFit on top of Hadoop 2.6.0. An extensive
empirical assessment over the Amazon EC2 platform on a variety of real-world
benchmarks shows that NearestFit is practical w.r.t. space and time overheads
and that its accuracy is generally very good, even in scenarios where
competitors incur non-negligible errors and wide prediction fluctuations.
Overall, NearestFit significantly improves the current state-of-art on progress
analysis for MapReduce
Actors vs Shared Memory: two models at work on Big Data application frameworks
This work aims at analyzing how two different concurrency models, namely the
shared memory model and the actor model, can influence the development of
applications that manage huge masses of data, distinctive of Big Data
applications. The paper compares the two models by analyzing a couple of
concrete projects based on the MapReduce and Bulk Synchronous Parallel
algorithmic schemes. Both projects are doubly implemented on two concrete
platforms: Akka Cluster and Managed X10. The result is both a conceptual
comparison of models in the Big Data Analytics scenario, and an experimental
analysis based on concrete executions on a cluster platform
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