1,661 research outputs found
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
String-net condensation: A physical mechanism for topological phases
We show that quantum systems of extended objects naturally give rise to a
large class of exotic phases - namely topological phases. These phases occur
when the extended objects, called ``string-nets'', become highly fluctuating
and condense. We derive exactly soluble Hamiltonians for 2D local bosonic
models whose ground states are string-net condensed states. Those ground states
correspond to 2D parity invariant topological phases. These models reveal the
mathematical framework underlying topological phases: tensor category theory.
One of the Hamiltonians - a spin-1/2 system on the honeycomb lattice - is a
simple theoretical realization of a fault tolerant quantum computer. The higher
dimensional case also yields an interesting result: we find that 3D string-net
condensation naturally gives rise to both emergent gauge bosons and emergent
fermions. Thus, string-net condensation provides a mechanism for unifying gauge
bosons and fermions in 3 and higher dimensions.Comment: 21 pages, RevTeX4, 19 figures. Homepage http://dao.mit.edu/~we
A Comparison of Big Data Frameworks on a Layered Dataflow Model
In the world of Big Data analytics, there is a series of tools aiming at
simplifying programming applications to be executed on clusters. Although each
tool claims to provide better programming, data and execution models, for which
only informal (and often confusing) semantics is generally provided, all share
a common underlying model, namely, the Dataflow model. The Dataflow model we
propose shows how various tools share the same expressiveness at different
levels of abstraction. The contribution of this work is twofold: first, we show
that the proposed model is (at least) as general as existing batch and
streaming frameworks (e.g., Spark, Flink, Storm), thus making it easier to
understand high-level data-processing applications written in such frameworks.
Second, we provide a layered model that can represent tools and applications
following the Dataflow paradigm and we show how the analyzed tools fit in each
level.Comment: 19 pages, 6 figures, 2 tables, In Proc. of the 9th Intl Symposium on
High-Level Parallel Programming and Applications (HLPP), July 4-5 2016,
Muenster, German
Gunrock: A High-Performance Graph Processing Library on the GPU
For large-scale graph analytics on the GPU, the irregularity of data access
and control flow, and the complexity of programming GPUs have been two
significant challenges for developing a programmable high-performance graph
library. "Gunrock", our graph-processing system designed specifically for the
GPU, uses a high-level, bulk-synchronous, data-centric abstraction focused on
operations on a vertex or edge frontier. Gunrock achieves a balance between
performance and expressiveness by coupling high performance GPU computing
primitives and optimization strategies with a high-level programming model that
allows programmers to quickly develop new graph primitives with small code size
and minimal GPU programming knowledge. We evaluate Gunrock on five key graph
primitives and show that Gunrock has on average at least an order of magnitude
speedup over Boost and PowerGraph, comparable performance to the fastest GPU
hardwired primitives, and better performance than any other GPU high-level
graph library.Comment: 14 pages, accepted by PPoPP'16 (removed the text repetition in the
previous version v5
Bounds on series-parallel slowdown
We use activity networks (task graphs) to model parallel programs and
consider series-parallel extensions of these networks. Our motivation is
two-fold: the benefits of series-parallel activity networks and the modelling
of programming constructs, such as those imposed by current parallel computing
environments. Series-parallelisation adds precedence constraints to an activity
network, usually increasing its makespan (execution time). The slowdown ratio
describes how additional constraints affect the makespan. We disprove an
existing conjecture positing a bound of two on the slowdown when workload is
not considered. Where workload is known, we conjecture that 4/3 slowdown is
always achievable, and prove our conjecture for small networks using max-plus
algebra. We analyse a polynomial-time algorithm showing that achieving 4/3
slowdown is in exp-APX. Finally, we discuss the implications of our results.Comment: 12 pages, 4 figure
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