software training courses 2010 corsi di addestramento ... - EnginSoft

Large model with non-linear material and deformations example solved on a
64 nodes cluster system
Common bottleneck sources
As the CAE industry continues an aggressive platform
migration from proprietary Unix servers to commodity HPC
clusters, CAE models are becoming more realistic, too,
requiring clusters to handle ever-increasing volumes of I/O
and the movement of large files.
As organizations rapidly expand their cluster deployments,
many encounter I/O bottlenecks when using legacy network
attached storage (NAS) architectures.
Initially, these NAS systems offered advantages such as shared
storage and simplified IT administration which reduced costs,
but today a few of them provide the scalability required for
effective I/O performance in parallel CAE simulations.
Recently, a new class of shared parallel storage technology has
developed to remove serial bottlenecks and to improve i/o
performances, therefore extending the overall scalability of
CAE simulations on clusters.
Parallel storage is the leading solution of parallel
NAS and enables the most advanced and I/O
demanding CAE challenges to become practical
applications. Some examples include the highfidelity
transient CFD, large eddy simulation
(LES), aerocoustics, large DOF structural dynamic
response, parameterized non-deterministic CAE
simulations for design optimization and the
coupling of CAE disciplines such as fluid-structure
interaction (FSI). CAE workflows are
overburdened with lost productivity when
engineers and scientists must wait for serial I/O
operations and large file transfers to complete.
Furthermore, as simulation and workflow
performance degrades, so does CAE analyst
efficiency and effective workgroup collaboration.
A parallel storage eliminates the I/O bottlenecks
with a cost-saving solution that restores
productivity and drives analyst creativity.
The benefits of parallel I/O for transient CFD were
demonstrated with a production case of an ANSYS
aerodynamics model of 111M cells, provided by
Newsletter EnginSoft Year 6 n°4 - 41
an industrial truck vehicle manufacturer. Figure 2 below,
illustrates the I/O schematic of the performance tests that
were conducted, which comprised a case file read, a compute
solve of 5 time steps with 100 iterations and a write of the
data file. In a full transient simulation the solve and write
tasks would be repeated to a much larger number of time steps
and iterations, and with roughly the same amount of
computational work for each of these repeatable tasks.
It is important to note that the performance of CFD solvers
and the numerical operations are not affected by the choice of
the file system, which only performs I/O operations. That is, a
CFD solver will perform the same on a given cluster regardless
of whether a parallel or serial NFS file system is used. The
advantage of parallel I/O is best illustrated in a comparison of
the computational profiles of each scheme. ANSYS CFD 12 on
PanFS keeps the I/O percent of the total job time in the range
of 3% at 64 cores to 8% at 256 cores, whereas 6.3 and NFS
spend as much as 50% of the total job time in I/O.
Visualization and Postprocessing
Another relevant matter of large cluster is visualization and
post-processing of results on relatively slow networks. An
effective solution is performing 3D renders with openGL inside
the cluster and giving the client the possibility of remote
Display.
VirtualGL is an open source package which gives any Unix or
Linux remote display software the ability to run OpenGL
applications with full 3D hardware acceleration. Some remote
display software, such as VNC, lacks the ability to run OpenGL
applications at all.
Tipical cluster management system and visualization nodes