Automated Design Exploration and Optimization + HPC Best Practices

Transcription

1 Automated Design Exploration and Optimization + HPC Best Practices ANSYS, Inc. June 8, 2012 Laz Foley & Simon Pereira Confidence by Design Detroit June 5, 2012

2 Outline The Path to Robust Design ANSYS DesignXplorer Mesh Morphing and Optimizer RBF Morph Adjoint Solver HPC Best Practices

3 The Path to Robust Design bust Design is an SYS Advantage gle Physics lution curacy, robustness, ed Multiphysics Solution Integration Platform What if Study Parametric Platform Design Exploration DOE, Response Surfaces, Correlation, Sensitivity, Unified reporting, etc. Optimization Algorithms Published API Robust Design Six Sigma Analysis Probabilistic Algorithms Adjoint solver methods

4 Deformation Single Physics/Multi Physics physics is the entry point ulation Physics enables real l prototyping Fluid Pressure Distribution 2 way coupled with a transient structural solution Stress? magnetic Force Density with Thermal Stress and Electromagnetic Force load

5 What If? Interactively adjust the parameter values and Update Needed for "What If?" Parametric CAD Connections Pervasive Parameters Persistent Updates Managed State, Update Mechanisms Remote Solve Manager (RSM)

6 Design Exploration

7 Optimization Optimal Candidates

8 ANSYS DesignXplorer Initial vs. Optimized Design Output Initial Design Optimized T t Ratio p t Ratio η [%] Power [MW] Engineers can easily appreciate the value of understanding.

9 Uncertainty of input Response Surface showing the effect of engine speed and thickness at outlet on Thermal Stress Pressure & Flow Velocity Parametric Geometry ust manifold design Sigma Analysis um Displacement should not exceed 1.5 mm arameters Response Parameters Diameter of the Max Flow Temperature d Max Deformation ess at inlet Max Von Mises stress l Temperature RPM Deformation All samples reports max deformation below 1.5 mm

10 Where are you? may want to consider how far along the path your simulation practices are aps you could improve your use of optimization and greater leverage your investment in Simulation?

11 ANSYS DesignXplorer Integral with Workbench Parametric multiphysics modeling with automated updates Bi directional CAD, RSM, scripting, reporting and more...

12 Robust Design Tools at ANSYS SYS DesignXplorer nified Workbench solution SYS Fluent uilt in Mesh Morphing and hape Optimization (MMO) tools Being hooked up to DX for 14.5 djoint solver Baseline Design High sensitivity changes to shape have a big effect on drag Low sensitivity R14 Optimized Design High sensitivity Shape on Downforce

13 Robust Design Tools at ANSYS SOFT Optimetrics roduce families of curves imultaneous solve with DSO packs ccess to EBU Adjoint d more Ansoft Corporation Fm [newton] XY Plot 1 Maxwell3DDesign1 Curve Info Fm Setup1 : LastAdaptive Gap='0.001in' Fm Setup1 : LastAdaptive Gap='0.002in' Fm Setup1 : LastAdaptive Gap='0.003in' Fm Setup1 : LastAdaptive Gap='0.004in' Fm Setup1 : LastAdaptive Gap='0.005in' Fm Setup1 : LastAdaptive Gap='0.006in' ampturns [A]

14 Optimization Partners SYS simulation software has been effectively used in concert with many optimization partners MATLAB (Mathworks) ModeFrontier (Esteco) OptiSLang (Dynardo) eartius Optimus (Noesis) RBF Morph Sculptor (Optimal) Sigma Technology (IOSO) TOSCA (FE DESIGN) isight (Dassault) Qfin (Qfinsoft)

15 ANSYS DesignXplorer

16 ANSYS DesignXplorer DesignXplorer is everything under this Parameter bar Low cost & easy to use! It drives Workbench Improves the ROI! ANSYS Workbench Solvers DX

17 Design of Experiments With little more effort than for a single run, you can use DesignXplorer to create a DOE

18 Correlation Matrix Understand how your parameters are correlated/influenced by other parameters!

19 Sensitivity Understand which parameters your design is most sensitive to!

20 Response Surface Understand the nsitivities of the output rameters (results) wrt he input parameters. 3D Response 2D Slices Response

21 Goal Driven Optimization Use an optimization algorithm or screening to understand tradeoffs or discover optimal design candidates!

22 Robustness Evaluation Input parameters have variation! Make sure your design is robust! Six Sigma, TQM t eters lso! nderstand how ur performance ill vary with your Predict how many parts will likely fail? Understand which inputs require the

23 Industry Testimonials The ease of using simulation tools has helped to transform our organization from a test centric culture to an analysis centric culture. Bob Tickel, Director of Analysis at Cummins This technology makes it possible to quickly evaluate hundreds of designs in batch processes to explore the complete design space so that we know we have the best possible design. Stresses Ken Karbon, Staff Engineer, General Motors r the course of the design process, Dyson s engineers steadily improved the ormance of the fan to the point that the final design has an amplification ratio of 15 ne, a 2.5 fold improvement over the six to one ratio of the original concept design. team investigated 200 different design iterations using simulation, which was 10 s the number that would have been possible had physical prototyping been the ary design tool. Physical testing was used to validate the final design, and the lts correlated well with the simulation analysis.

24 Example 1: Slit Die eed uniform outflow inimize pressure drop P2 P3 P1 BAD Bad GOOD Good Pressure Drop

25 Example 2: Combustor 3 parameters Diffuser Length Exit Height Outlet Minimize pressure loss Outlet Minimize mach number Outlet Inlet Dump Gap Sensitivity

26 Mesh Morphing and Optimizer

27 Fluent Morpher Optimization Feature llows users to optimize product esign based on shape eformation to achieve design bjective ased on free form deformation ool coupled with various ptimization methods

28 Mesh Morphing ies a geometric design change directly to the mesh in the solver a Bernstein polynomial based morphing scheme eform mesh deformation defined on a matrix of control points leads to a oth deformation rks on all mesh types (Tet/Prism, CutCell, HexaCore, Polyhedral) prescribes the scale and direction of deformations to control oints distributed evenly through the rectilinear region.

29 Process What if? Setup Case Run Setup Morph Morph Evaluate Choose OR Regions Parameters Deformation Optimizer Optimizer Setup Case Run Setup Optimizer Optimize Auto Optimal

30 Deformation Definition Define constraint(s) (if any) Select control points and prescribe the relative ranges of motion

31 Objective Function Objective Function: Equal flow rate Baseline Design Optimized Design

32 Optimize! Optimizer Algorithms; Compass, Powell, Rosenbrock, Simplex, Torczon to

33 Example: L Shaped Duct Application: L shaped duct Objective Function: Uniform flow at the outlet Significant Improvement in Flow Uniformity

34 RBF Morph

35 How Does RBF Morph work? A system of radial functions is used to fit a solution for the mesh movement/morphing, from a list of source points and their prescribed displacements Radial Basis Function interpolation is used to derive the isplacement at any location in the space The RBF problem definition is mesh ndependent.

36 RBF Morph is Integrated with Fluent

37 Example 1: Internal Flow

38 Example 3: External Flow

39 Example 4: 50:50:50 Optimisation Courtesy of Volvo Cars ecently conducted conceptual study by ANSYS in conjunction with Volvo Cars 0 Million cell hybrid mesh of Volvo XC60 0 Design variants investigated using RBF Morph Addon for ANSYS Fluent and orkbench Design Explorer 0 hours total clock time to complete full optimisation on HPC Cluster

40 External Aerodynamics lvo XC60 vehicle model our shape parameters BF Morph to define shape arameters NSYS DesignXplorer To drive shape parameters To create DOE To perform Goal Driven Optimization al Aerodynamics timization Process Capacity Automatic Fast

41 Step #1 : Baseline Model olume Mesh TGrid ell Count : 50.2 Million Cells rism Layers : 10 (First Aspect Ratio 10, rowth 1.1) rism Count : 24.4 Million Cells ewness < 0.9 Prism Layers Cut Plane Y=0

42 Step #2 : CFD Setup undary Conditions let : Velocity Inlet 100 kmph utlet : Pressure Outlet, 0 Pa (Gage) ide walls : Wall, no slip op wall : Wall, no slip lver Settings teady, PBCS, Green Gauss Node ased Gradient luid : Incompressible air, ensity = kg/m 3, urbulence : Realizable K epsilon, on equilibrium wall treatment iscretization : Pressure Standard Momentum, TKE, TDR 2 nd Order Solution Controls Courant Number = 200 ERF Momentum, Pressure = 0.75 URFs Density = 1.0, Body Forces = 1.0

43 Step #3 : RBF Morph Boat Tail Angle (P2) Roof Drop Angle (P3)

44 Step #3 : RBF Morph

45 Step #3 : RBF Morph Fully integrated within FLUENT and Workbench Easy to use Parallel => rapidly morph large size models Mesh independent solution works with all element types (tetrahedral, hexahedral, polyhedral, etc.) Superposition of multiple RBF-solutions makes the FLUENT case truly parametric (only 1 mesh is stored) RBF-solution can also be applied on the CAD Precision: exact nodal movement and exact feature preservation.

46 Step #4 : Setup DesignXplorer Central Composite Design, Face entered, Enhanced

47 Step #5 : Running Simulations 768 Cores 384 Cores 288 Cores 240 Cores 144 Cores Task Time (Seconds) Time (Seconds) Time (Seconds) Time (Seconds) Time (Seconds) volume mesh of baseline into the CFD solver and solver settings Baseline Case (i.e. Design Point 1) Solution ing CFD data file Each Subsequent Design Point h vehicle shape Solution ng CFD data file l Run Time (Wall Clock) ded for All 50 Design ts (Hours)

48 Results :

49 Optimization

50 Results : ow Results Discussion Design point 1, 9, 19 & 25 Velocity contours Iso surface of total pressure = 0.0 Design Points Boat Tail Angle (P2) Long Roof Angle (P3) Green House (P4) Front Spoiler Angle (P5) Drag Force (N) (P1)

51 Design Point #1

52 Design Point #19

53 Design Point #25

54 Adjoint Solver

55 Adjoint Solution? An adjoint solver allows specific information about a fluid system to be computed that is very difficult to gather otherwise. The adjoint solution itself is a set of derivatives. They are not particularly useful in their raw form and must be post processed appropriately. The derivative of an engineering quantity with respect to all of the inputs for the system can be computed in a single calculation. Example: Sensitivity of the drag on an airfoil to its shape. There are 4 main ways in which these derivatives can be used: 1. Qualitative guidance on what can influence the performance of a system strongly. 2. Quantitative guidance on the anticipated effect of specific design changes. 3. Guidance on important factors in solver numerics. 4. Gradient based design optimization.

56 How to Use the Results Qualitative GOAL: Identify features of a system design that are most influential in the performance of the system. EXAMPLE: Sensitivity of the Drag on a NACA 0012 airfoil to changes in the shape of the airfoil. The shape sensitivity field is extracted from the adjoint solution in a post processing step. High sensitivity changes to shape have a big effect on drag Low sensitivity changes to shape have a small effect on drag

57 How to Use the Results Quantitative GOAL: Identify specific system design changes that benefit the performance and quantify the improvement in performance that is anticipated. EXAMPLE: Design modifications to turning vanes in a 90 degree elbow to reduce the total pressure drop. The optimal adjustment that is made to the shape is defined by the shape sensitivity field (steepest descent algorithm). Effect of each change can be computed in advance based on linear extrapolation. Baseline Modified Original P = Pa Expected change computed using the adjoint and linear extrapolation = 10.0 Pa Make the change and recompute the solution. Actual change = 9.0 Pa

58 How to Use the Results Solver Numerics GOAL: Identify aspects of the solver numerics and computational mesh that have a strong influence on quantities that are being computed that are of engineering interest. EXAMPLE: Use the adjoint solution to identify parts of the mesh where mesh adaption will benefit the computed drag by reducing the influence of discretization errors. Baseline Mesh Adapted Mesh Adapted Mesh Detail

59 How to Use the Results Optimization GOAL: Perform a sequence of automated design modifications to improve a specific performance measure for a system EXAMPLE: Gradient based optimization of the total pressure drop in a pipe. Flow solution is recomputed and the adjoint recomputed at each design iteration Initial design Final design 30% reduction in total pressure drop after 30 design iterations p tot [Pa]

60 Mesh Morphing Once a desired change to the geometry of the system has been selected, how is that change to be made? Mesh morphing provides a convenient and powerful means of changing the geometry and the computational mesh. Use Bernstein polynomial based morphing scheme discussed earlier

61 Mesh Morphing & Adjoint Data xample: Sensitivity of lift to surface hape Flow elect portions of the geometry to be odified djoint to deformation operation Surface shape sensitivity becomes control point sensitivity (chain rule for differentiation) enefit of this approach is two fold Smooths the surface sensitivity field Provides a smooth interior and boundary mesh deformation

62 Moveable walls Mesh Morphing, Adjoint Data & Constraints The adjoint solution is determined based on the specific flow physics of the problem in hand. The effect of other practical engineering constraints must be reconciled with the adjoint data to decide on an allowable design change. Example: Some walls within the control volume may be constrained not to move. A minimal adjustment is made to the control point sensitivity field so that deformation of the fixed walls is eliminated. Fixed wall Fixed wall

63 Current Functionality adjoint solver is released with all Fluent 14 packages. cumentation is available heory sage utorial ase study ining is available ctionality is activated by Loading the adjoint solver add on module ew menu item is added at the top level

64 Current Functionality Application Drivers y initial application areas are: Low speed external aerodynamics F1 (increase downforce) Production automobiles (decrease drag) Low speed internal flows Total pressure drop (reduce losses) Fluent 14.5 a mechanism for users to fine a wide range of observables of terest will be provided. Forces Moments Pressure drop Swirl Ratios Products Variances Linear combinations Unary operations

65 Current Scope SYS Fluent flow solver has very broad scope joint is configured to compute solutions based on some assumptions teady, incompressible, laminar flow. teady, incompressible, turbulent flow with standard wall functions. irst order discretization in space. rozen turbulence. e primary flow solution does NOT need to be run with these restrictions trong evidence that these assumptions do not undermine the utility of the adjoint olution data for engineering purposes. lly parallelized. adient algorithm for shape modification esh morphing using control points. joint based solution adaption

66 Example 1: Automotive Aerodynamics Surface map of the drag sensitivity to shape changes

67 Example 2: Pressure Drop in a Duct Total Pressure Drop (Pa) Geometry Predicted Result Original 22.0 Modified ggressive adjustment results in a 17% reduction in loss in just one design iteration

68 HPC Best Practices

69 Guidelines: Know your hardware lifecycle Have a goal in mind for what you want to achieve Using Licensing productively Using ANSYS provided processes effectively

70 Hardware Considerations This section is meant to provide an overview of the different hardware components and how they can effect solution time. Hopefully this will give you some of the tools to understand why some of the benchmark numbers in better detail. ANSYS would always recommend that the best thing to do before buying a system is to look at the latest benchmarks. If you are not sure please ask.

71 Effect of Clock Speed Impact of CPU Clock on Application Performance Processor: Xeon X5600 Series Hyper Threading: OFF, TURBO: ON Active cores: 12/node; Memory speed: 1333 MHz (performance measure is improvement relative to CPU Clock 2.66 GHz) Higher is better Improvement due to Clock GHz 2.93 GHz 3.47 GHz Clock Ratio eddy_417k aircraft_2m turbo_500k sedan_4m truck_14m

72 Effect of Memory Speed We can see here the effect of memory speed. This has implications on how you build your hardware. Some processors types have slower memory speeds by default. On other processors nonoptimally filling the memory channels can slow the memory speed. Impact of Memory Speed 130% 125% 120% 115% 110% 105% 100% 95% 90% 85% 80% Impact of DIMM speed on ANSYS/FLUENT Application Performance (Intel Xeon x5670, 2.93 GHz) Hyper Threading: OFF, TURBO: ON Active threads per node: 12 (performance measure improvement is relative to memory speed of 1066 MHz) eddy_417k turbo_500k aircraft_2m sedan_4m truck_14m ANSYS/FLUENT Model 1066 MHz 1333 MHz

73 Turbo Boost (Intel) / Turbo Core (AMD) Turbo Boost (Intel)/ Turbo Core(AMD) is a form of over clocking that allows you to give more GHz to individual processors when others are idle. With the Intel s have seen variable performance with this ranging between 0 8% improvement depending on the numbers of cores in use. The graph below for CFX on a Intel X5550. This only sees a maximum of 2.5% improvement.

74 Hyper Threading: ANSYS Fluent Hyper Threading Technology makes a single physical processor appear as two logical processors. Evaluation of Hyperthreading on ANSYS/FLUENT Performance idataplex M3 (Intel Xeon x5670, 2.93 GHz) TURBO: ON (measurement is improvement relative ot Hyperthtreading OFF) This is not the same as physically having two logical processors and does not give double the speedup. In our tests we ve seen as high as a 20% increase in performance although you can see the actual performance can be quite variable from the graph opposite. Higher is better Improvemet due to Hyperthreading HT OFF (12 threads on 12 physical cores) HT ON (24 threads on 12 physical cores) It is worth noting that this has licensing implications as you would need to oversubscribe the physical cores and hence would need double the HPC Licenses eddy_417k turbo_500k aircraft_2m sedan_4m truck_14m ANSYS/FLUENT Model

75 AMD vs. Intel Traditionally Intel take the power approach in general in their 2 socket systems (faster core but less of them per processor/socket). Traditionally AMD take the economies of scale approach (more cores per processor but individually slower clock speeds). Remember that this landscape changes because they are constantly in competition with each other. Please note that whilst we do have some numbers for the new Intel Sandy bridge chips we do not have scaling numbers for the equivalent AMD 6200 series at the time of writing this presentation.

76 2 Socket vs. 4 Socket Systems Current 4 socket systems come up slower than their 2 socket counterparts (based on Intel Westmere vs. Xeon E7 8837). Clock speed slower Memory speed slower No additional memory bandwidth. Performance of ANSYS Fluent on two socket and four socket based systems Performance measure is Fluent Rating (higher values are better) 2 socket based Systems 4 socket based Systems HS22/HS22V Blade, 3550/3650 M3, Dx360 M3 IBM HX5 Blade, X3850 (Xeon 5600 Series) (Xeon E series) Fluent Fluent des Sockets Cores Nodes Sockets Cores Rating Rating

77 Effect of the Interconnect When going for multiple systems linked together the interconnect becomes an important factor. The interconnect is the fabric that connects the nodes. We can see from the graph opposite with FLUENT how quickly the performance of Gigabit Ethernet drops off. FLUENT Rating Higher is better ANSYS/FLUENT Performance idataplex M3 (Intel Xeon x5670, 12C 2.93 GHz) Network: Gigabit, 10-Gigabit, 4X QDR Infiniband (QLogic, Voltaire) Hyperthreading: OFF, TURBO: ON Models: truck_14m QLogic Voltaire 10-Gigabit Gigabit Number of Cores used by a single job

78 ANSYS Fluent Auto Partitioning Time to Partition cavity 200M case, Cavity 768 Case coresover 768 cores o partitioning is now very quick s than 10s to process 800M cells! ial pre partitioning step no ger required Time in seconds Time in seconds M 400M 600M 800M Time Time to Partition truck_111m 111M Truck Case

79 ANSYS CFX Partitioning timize parallel partitioning in multi core clusters (CFX) β artitioner determines number of connections etween partitions and optimizes part. host ssignments P2 P1 P4 P3 P6 P5 P7 P8 Partitioning step finds adjacency amongst partitions; partitions with max adjacency are grouped on same compute nodes use previous results to initialize calculations on large problem (CFX) β arge case interpolation for cases with >~100M odes P1 P3 P2 P7 P5 P6 P4 P8 Compute Node 1 Compute Node 2 an up of coupled partitioning option for multi domain cases (CFX) liminates isolated partition spots matically reduced partitioning times for cases h fluid solid interfaces and very large numbers of ions

80 ANSYS Fluent Parallel Scalability sistently improved scalability ss releases Intel Harpertown an, 4M cells 2.80GHz (Nehalem EP) Intel Westmere

81 ANSYS Fluent Parallel Scalability sistently improved scalability ss releases ck, 111M cells Intel Harpertown ICE 8400EX, Intel 6 core Intel Westmere hex core 2.93 GHz

82 ANSYS Fluent Parallel Scalability on Intel SYS Fluent 14 ive Performance r is better eomean core Xeon X core Xeon E eomean 1 Sedan_4m Truck_14m 1.53 Leading Performance for fluid flow simulation The memory bandwidth of the Intel Xeon processor E product family allows excellent scalability and per core performance. Support for higher speed memory DIMMs, added on-core capacity for memory loads, as well as a larger cache size are key to extending performance and scalability. Higher memory bandwidth has a pronounced impact with fully coupled solver applications, which are the most memory intensive. Sedan_4m is shown as an example of fully coupled solver performance. Truck_14m is representative of segregated solver performance. The horizontal line at 1.63 represents the geomean speedup over 6 standard benchmarks. 6 core Xeon X core Xeon E5 2680

83 ANSYS CFX Parallel Scalability on Intel Intel Xeon 5650 Intel Xeon E Good scalability and more operations per clock make obtaining results on Intel Xeon E5 1.68x faster than on Intel Xeon 5600 platforms For end user it is about faster turnaround or solving larger tasks with the same resources along with lower TCO AirliftReactor BigPipe CombBVM CombEDM Cylinder IndyCar Internal LeMansCar LES_001 Pump RadCity RadFurnace tagecompressor aticmixer100mm StaticMixer100 StaticMixer200 taticmixer 400k Turbine Wigley100 Source: Published/submitted/approved results as of March 6, Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may

84 Example data for scaling with R14 monitors Including Monitors 3072 cores lability with Monitors Scalability to higher core counts Simulations with monitors including plotting and printing Hex core mesh, F1 car, 130 million cells monitor enabled Monitor support optimizations maintain scalability expectations

85 Fluids I/O ENT, CFX and AUTODYN use a singular structure. his means there is one global set of files and very process writes to them. Serial I/O ANSYS FLUENT Parallel I/O s methodology falls down at a large ber of cores where the file I/O becomes ottleneck. FX deals with this by using inline compression dat) LUENT has both inline compression (cdat) and t v12.x introduced support for a Parallel File dat). allel file system support in ANSYS ENT ~10x 20x speedup for data write Eliminates scaling bottleneck for data

86 HPC Fluids Demonstration Case o Demonstrate 50:50:50 Method Volvo XC60 vehicle model Four shape parameters RBF Morph (Integrated within FLUENT) to define shape parameters Grid morphing in parallel NSYS WorkBench (Frame Work o Automate Process) To drive shape parameters To create DOE To perform Goal Driven Optimization The 50:50:50 Method 50 design points in the design space 50 million cells used in CFD simulation of each design point 50 hours total elapsed time to simulate all the design points EXTENT ACCURACY SPEED One Click Entire design space is simulated and postprocessed completely automatically after the initial baseline case setup

87 HPC Fluids Demonstration Case STEP 1 Prepare Meshed Model for Baseline Vehicle Shape STEP 2 CFD Solver Setup, Define Shape Parameters STEP 3 Generate DOE using Input Shape Parameters STEP 4 Mesh Morpher Integrated within FLUENT Solver (FLUENT), Optimizer (DX) & Post Processor (CFD Post) Morph Integrated Vehicle within Shape ANSYS WorkBench Run CFD Simulation Collate Data,

88 HPC Fluids Demonstration Case 768 Cores 384 Cores 288 Cores 240 Cores 144 Cores Task Time (Seconds) Time (Seconds) Time (Seconds) Time (Seconds) Time (Seconds) volume mesh of baseline into the CFD solver and solver settings Baseline Case (i.e. Design Point 1) Solution ing CFD data file Each Subsequent Design Point h vehicle shape Solution ng CFD data file l Run Time (Wall Clock) ded for All 50 Design ts (Hours)

89 HPC Fluids Demonstration Case Compute Cluster Details 1. Intel s Endeavor Cluster 2. Intel Xeon X5670 (dual socket) 3. Clock speed 2.93 GHz 4. Six cores per socket (12 cores per node) GB 1333 MHz, SMT ON, Turbo ON 6. QDR Infiniband 7. RHEL Server Release 6.1

90 GPU Acceleration for CFD Radiation View Factor calculation (ANSYS FLUENT 14 beta) capability for specialty physics view factors, ray tracing, reaction rates, etc.

91 Getting the right setup is a balancing act..

92 Factors to Consider HPC Licensing Cost Cost of Hardware Complexity of Deployment and Maintenance

93 HPC Licensing Cost ANSYS HPC is licensed in either the HPC Workgroup/Enterprise (or individually) or HPC Packs. Given that it is licensed per partition (which in most cases translated to a core) the best value for money is in getting the best scalability per core as possible. When running multiple cores make sure you are using them as effectively as the memory bandwidth allows.

94 Cost of Hardware ANSYS will, in general, recommend the best hardware for performance that gets you the best out of your licensing investment. However you may need to make trade off's for your budget. 2 socket systems provide the best performance but more inherently more complexity (and hence cost) because of the need for high speed interconnects when in a cluster. Current 4 socket systems have less performance than their 2 socket counterparts but are also cheaper because of their lack of requirement for the high speed interconnects to get to higher numbers of nodes at the low end.

95 Complexity of Deployment and Maintenance A large cluster can have significant overheads in ease of deployment & on going maintenance costs. A 4 socket system, whilst having less performance, may provide an easier deployment and maintenance route at the lower end and will be a better fit to what the average IT department is used to. Often users get too caught up on per core performance at the detriment of not getting any extra speedup at all. It is important to purchase something you feel you can internally support. Purchase 3 rd party support for high performance clusters if you do not feel you have the skills to support it internally.

96 Remember the Following... If you opt for unsupported infrastructure This does not mean that it will not work but you use them at your own risk. We may ask you to replicate it on a system that is supported before providing further support if you run into problems! We recommend: Buying Supported Operating systems and Hardware Using ANSYS Supported Practices Talking to us before buying! It is in all our interests that you get this right!

97 Information Available SYS Partner Solutions hpc.asp Reference configurations Performance data White papers Sales contact points rformance Data

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99 Information Available e Manual ections on best practices and parallel rocessing for various solvers stallation walkthroughs for installing the roducts, parallel processing, licensing and RSM emote solve manager) SYS Advantage nline Magazine

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101 Automated Design Exploration and Optimization + HPC Best Practices