Hardware platforms of parallel and distributed simulation technology
A.B.Degtyarev, Wunna Kyaw, Myo Min Swe
abdegtyarev@gmail.com,wna.ru.pol@gmail.com,eltson@gmail.com
Marine Technical University of Saint-petersburg, Russia Federation
Abstarct
CPU cache
This work carried out the hardware platforms of parallel and distributed
simulation technology of interest here contain a potentially large number of
processors interconnected through a communication network. Our research of
this work presents five categories: Parallel versus Distributed Computers,
Shared-Memory Multiprocessors, Distributed-Memory Multi-computers, SIMD
Machines, and Testing MPI program on Beowulf cluster with distributed
computers.
CPU cache
Memory
Memory
Testing MPI program on the cluster
And then we are testing MPI simple pi program on this cluster, we will see in Figure1.5,wall clock time in different processes.
Communication
controller
Communication
controller
Interconnection network
Fig.1.2 Block diagram of a typical distributed-memory multicomputer.
Parallel versus Distributed Computers
Multiple-CPU hardware platforms can be classified into two categories:
parallel and distributed computers. Here we can see the differences between
these platforms are summarized in Table 1.
TABLE 1. Contrasting parallel and distributed computers
Physical extent
Processors
Communication
network
Communication
Latency
Parallel computers
Machine Room
Distributed computers
Single building to global
Homogenous
Often heterogeneous
Customized Switch Commercial LAN or WAN
Less than 100
microseconds
Fig-1.5 wall clock time in different processes
SIMD machines
The central characteristic of these machines is that all processors must execute the same
instruction (but using different data) at any instant in the program's execution. SIMD machines
typically contain more, albeit simpler, processors (processing elements) than either
multiprocessors or multicomputers. Because they are simpler than complete microprocessors,
custom-designed components are usually used rather than off-the-shelf parts. A block diagram
for a typical SIMD machine is depicted in Figure 1.3.
Hundreds of microseconds
to seconds
And also we can see in figure-1.5,there is measure time required to transmit data around
a ring of processes. N double precision value were sent in a ring transmission starting
and ending at process 0 and here we are using of total 8 , 12 and 16 processes. Any
MPI application can use this cluster to distribute the process among multiple
computers.
Instruction memory
Fig-1.6 measuring time in a ring transmission of different processes
Control unit
Shared-memory multiprocessors
Shared-memory multiprocessors, distributed memory multi-computers,
and SIMD machines provide different programming models to the
application. One type of shared-memory machine, the symmetric
multiprocessor (SMP), has become increasingly popular. A typical sharedmemory machine is depicted in Figure 1.1.
CPU cache
CPU cache
Processing
element
Processing
element
Processing
element
Data
memory
Data
memory
Data
memory
Conclusion
Here we want to say that parallel and distributed simulation technology can provide
substantial benefit in situations such as the following:
1. Time critical applications where simulations are used as decision aids (for example, how
do I re-route air traffic?), and results are needed on very short notice.
2. Design of large and/or complex systems where execution of the simulation program is
excessively time-consuming.
3. Virtual environments such as for training, where participants and/or resources are at
geographically distant locations.
CPU cache
Interconnection network
Fig.1.3 Block diagram of a typical SIMD machine
Interconnection network
Memory
Memory
I/O devices
Building Own Beowulf cluster with distributed computers
Fig.1.1 Block diagram of a typical shared-memory multiprocessor
Distributed-memory multicomputers
Multicomputers do not support shared variables. All communications
between processors must occur via message passing. Message-passing
libraries are provided to send and receive messages between processors. A
block diagram for a typical distributed memory multicomputer is shown in
Figure 1.2. Each "node" of the network is not unlike that found in personal
computers; it includes a CPU cache, memory, and a communications
controller that handles interprocessor communication.
Firstly we are going to test MPI parallel computing program with distributed computers. So we are here
building simple Beowulf cluster (see in Fig1.4 ),we set up a system as four Pcs , Pc-1 as master and other as
slave nodes. Then we had been create SSH for our users. Our cluster nodes will communicate with SSH and
share information by mounting a shared NFS partition.
Pc1
Ubuntu-12.04
Public
network
NFS shared
Ethernet
controller
Pc2
Ubuntu12.04
Pc3
Ubuntu12.04
Figure 1.4 Beowulf cluster with distributed computers
Pc4
Ubuntu12.04
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