2,171 research outputs found
Efficient checkpointing over local area networks
Parallel and distributed computing on clusters of workstations is becoming very popular as it provides a cost effective way for high performance computing. In these systems, the bandwidth of the communication subsystem (Using Ethernet technology) is about an order of magnitude smaller compared to the bandwidth of the storage subsystem. Hence, storing a state in a checkpoint is much more efficient than comparing states over the network.
In this paper we present a novel checkpointing approach that enables efficient performance over local area networks. The main idea is that we use two types of checkpoints: compare-checkpoints (comparing the states of the redundant processes to detect faults) and store-checkpoints (where the state is only stored). The store-checkpoints reduce the rollback needed after a fault is detected, without performing many unnecessary comparisons.
As a particular example of this approach we analyzed the DMR checkpointing scheme with store-checkpoints. Our main result is that the overhead of the execution time can be significantly reduced when store-checkpoints are introduced. We have implemented a prototype of the new DMR scheme and run it on workstations connected by a LAN. The experimental results we obtained match the analytical results and show that in some cases the overhead of the DMR checkpointing schemes over LAN's can be improved by as much as 20%
Fault-Tolerant Adaptive Parallel and Distributed Simulation
Discrete Event Simulation is a widely used technique that is used to model
and analyze complex systems in many fields of science and engineering. The
increasingly large size of simulation models poses a serious computational
challenge, since the time needed to run a simulation can be prohibitively
large. For this reason, Parallel and Distributes Simulation techniques have
been proposed to take advantage of multiple execution units which are found in
multicore processors, cluster of workstations or HPC systems. The current
generation of HPC systems includes hundreds of thousands of computing nodes and
a vast amount of ancillary components. Despite improvements in manufacturing
processes, failures of some components are frequent, and the situation will get
worse as larger systems are built. In this paper we describe FT-GAIA, a
software-based fault-tolerant extension of the GAIA/ART\`IS parallel simulation
middleware. FT-GAIA transparently replicates simulation entities and
distributes them on multiple execution nodes. This allows the simulation to
tolerate crash-failures of computing nodes; furthermore, FT-GAIA offers some
protection against byzantine failures since synchronization messages are
replicated as well, so that the receiving entity can identify and discard
corrupted messages. We provide an experimental evaluation of FT-GAIA on a
running prototype. Results show that a high degree of fault tolerance can be
achieved, at the cost of a moderate increase in the computational load of the
execution units.Comment: Proceedings of the IEEE/ACM International Symposium on Distributed
Simulation and Real Time Applications (DS-RT 2016
Designing SSI clusters with hierarchical checkpointing and single I/O space
Adopting a new hierarchical checkpointing architecture, the authors develop a single I/O address space for building highly available clusters of computers. They propose a systematic approach to achieving a single system image by integrating existing middleware support with the newly developed features.published_or_final_versio
Fault Tolerant Adaptive Parallel and Distributed Simulation through Functional Replication
This paper presents FT-GAIA, a software-based fault-tolerant parallel and
distributed simulation middleware. FT-GAIA has being designed to reliably
handle Parallel And Distributed Simulation (PADS) models, which are needed to
properly simulate and analyze complex systems arising in any kind of scientific
or engineering field. PADS takes advantage of multiple execution units run in
multicore processors, cluster of workstations or HPC systems. However, large
computing systems, such as HPC systems that include hundreds of thousands of
computing nodes, have to handle frequent failures of some components. To cope
with this issue, FT-GAIA transparently replicates simulation entities and
distributes them on multiple execution nodes. This allows the simulation to
tolerate crash-failures of computing nodes. Moreover, FT-GAIA offers some
protection against Byzantine failures, since interaction messages among the
simulated entities are replicated as well, so that the receiving entity can
identify and discard corrupted messages. Results from an analytical model and
from an experimental evaluation show that FT-GAIA provides a high degree of
fault tolerance, at the cost of a moderate increase in the computational load
of the execution units.Comment: arXiv admin note: substantial text overlap with arXiv:1606.0731
COMPARATIVE ANALYSIS OF PVM AND MPI FOR THE DEVELOPMENT OF PHYSICAL APPLICATIONS IN PARALLEL AND DISTRIBUTED SYSTEMS
This research is aimed to explore each of these two Parallel Virtual Machine (PVM) and Message Passing Interface(MPI) vehicles for DPP (Distributed Parallel Processing) considering capability, ease of use, and availability, and compares their distinguishing features and also explores programmer interface and their utilization for solving real world parallel processing applications. This work recommends a potential research issue, that is, to study the feasibility of creating a programming environment that allows access to the virtual machine features of PVM and the message passing features of MPI. PVM and MPI, two systems for programming clusters, are often compared. Each system has its unique strengths and this will remain so in to the foreseeable future. The comparisons usually start with the unspoken assumption that PVM and MPI represent different solutions to the same problem. In this paper we show that, in fact, the two systems often are solving different problems. In cases where the problems do match but the solutions chosen by PVM and MPI are different, we explain the reasons for the differences. Usually such differences can be traced to explicit differences in the goals of the two systems, their origins, or the relationship between their specifications and their implementations. This paper also compares PVM and MPI features, pointing out the situations where one may be favored over the other; it explains the deference’s between these systems and the reasons for such deference’s
Sparse Allreduce: Efficient Scalable Communication for Power-Law Data
Many large datasets exhibit power-law statistics: The web graph, social
networks, text data, click through data etc. Their adjacency graphs are termed
natural graphs, and are known to be difficult to partition. As a consequence
most distributed algorithms on these graphs are communication intensive. Many
algorithms on natural graphs involve an Allreduce: a sum or average of
partitioned data which is then shared back to the cluster nodes. Examples
include PageRank, spectral partitioning, and many machine learning algorithms
including regression, factor (topic) models, and clustering. In this paper we
describe an efficient and scalable Allreduce primitive for power-law data. We
point out scaling problems with existing butterfly and round-robin networks for
Sparse Allreduce, and show that a hybrid approach improves on both.
Furthermore, we show that Sparse Allreduce stages should be nested instead of
cascaded (as in the dense case). And that the optimum throughput Allreduce
network should be a butterfly of heterogeneous degree where degree decreases
with depth into the network. Finally, a simple replication scheme is introduced
to deal with node failures. We present experiments showing significant
improvements over existing systems such as PowerGraph and Hadoop
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