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Best-in-class companies are taking advantage of Abaqus Unified FEA to consolidate their processes and tools, reduce costs and inefficiencies, and gain a competitive advantage.
Abaqus 6.14 3 full#
In the automotive industry engineering work groups are able to consider full vehicle loads, dynamic vibration, multibody systems, impact/crash, nonlinear static, thermal coupling, and acoustic-structural coupling using a common model data structure and integrated solver technology. The Abaqus Unified FEA product suite offers powerful and complete solutions for both routine and sophisticated engineering problems covering a vast spectrum of industrial applications. SIMULIA delivers a scalable suite of unified analysis products that allow all users, regardless of their simulation expertise or domain focus, to collaborate and seamlessly share simulation data and approved methods without loss of information fidelity.
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Abaqus 6.14 3 software#
The use of multiple vendor software products creates inefficiencies and increases costs. Today, product simulation is often being performed by engineering groups using niche simulation tools from different vendors to simulate various design attributes. Large Model (~77 TFLOPs), 4.71M DOF, Nonlinear Static, Direct Sparse SolverĪbaqus 6.14-2 with Intel Xeon E5-2697v2, 2.70 GHz CPU, 128 GB memory Tesla K40 GPUĪBAQUS PERFORMANCE WITH GPU No additional tokens for GPUĬPU Only CPU + K40 CPU + 2x K40 CPU + K80Ībaqus 6.14-2 with Intel Xeon E5-2697v2, 2.DS SIMULIA ABAQUS 6.14-3 WIN64-SSQ | 3.7 Gbĭassault Systemes, a world leader in 3D and Product Lifecycle Management (PLM) solutions, has released an SP03 for Abaqus 6.14, the leading unified FEA product suite from SIMULIA.
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GPUs help in reducing consumption of licensing tokensĪ single GPU board is treated as one core for token countĬPUs) 16 1 16 2 16 * # of Tokens = INT(5*cores^0.422)
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High-performance automatic multi-level substructuring eigensolverġ additional token unlocks additional CPU core OR unlocks 1 entire GPU Parallel direct sparse solver with dynamic load balancing Parallel execution on both shared memory & distributed memory parallel (cluster) systems Relaxation of memory requirements for GPUĪMS Recovery Phase - Recover full/partial eigenmodesĪMS Reduction Phase - Reduce the structure onto substructure modal subspacesĪMS Reduced Eigensolution Phase - Compute reduced eigenmodesĪMS: Automatic Multi-level Substructuring Hardware-accelerated graphics over LAN/WANĭirect Sparse solver DMP Split, less memory AMS Solver Reduced Eigen Phase Pre-processing+ Post-processing+Model tweaking Visualizing complex industrial design from a continent away Real-time interactive crash test visualization with no data transport integrated into the 3DEXPERIENCE platform Remote graphics from DS Cloud with GRID K2 with H264 HW encoding Showcase of a Proof Of Concept at SIGGRAPH 2014 with NVIDIA Hours to transfer dataMinutes to transfer data GPU Accelerator Optimized for Parallel TasksĮfficient handling of parallel tasks in matrix solutions Visualization, Accelerated Computing & Virtualization TESLA Accelerating Momentum in HPC and Big Data Analytics Faster Innovation - Accelerating SIMULIA Abaqus Simulations with NVIDIA GPUs