R&D Centers and Technology Companies HPC
Accelerate product simulation, materials research, and computational engineering workloads.
Private-sector R&D centers must reach a position where they can compete with academia in product simulation and materials research. Across a wide industry spectrum — from automotive suppliers to advanced materials manufacturers, defense sub-industries to chemical and pharmaceutical R&D — computational workloads share a common problem: desktop and small server infrastructure hits a bottleneck, iteration speed drops, and market opportunity slips away.
In a competitive product development cycle, reducing simulation solve time from 72 hours to 4 hours versus spending engineer days on waiting often coincides exactly with the difference between launching a product on time or not.
Why Do R&D Centers Need HPC?
Companies in Turkey with R&D Center status mostly try to handle intensive computational workloads with internal resources. This approach hits several bottlenecks:
- Workload size: Industrial-scale CFD, FEM, and MD simulations require dozens to hundreds of cores. Desktop workstations can only run a single analysis with heavily restricted mesh resolution.
- Parallel iteration: Design of Experiments (DoE) or parameter sweeps require computing many variants simultaneously. Sequential execution makes schedule pressure inevitable.
- High memory requirements: DFT calculations, large-mesh FEM analyses, and molecular dynamics scenarios can consume 256 GB–2 TB of RAM per node.
- Data management: Large simulation outputs (checkpoint files, solution fields) create I/O bottlenecks without fast parallel storage.
Cloud solutions can temporarily lift this load; however, for R&D centers with continuous workload profiles, on-premise or managed rental models are far more economical in the medium-to-long term. Additionally, under Turkey’s R&D regulations and KVKK compliance requirements, keeping sensitive project data with overseas providers carries legal and competitive risk.
Typical R&D HPC Workloads
Product Simulation and Engineering Analysis
The bulk of an R&D center’s computing load consists of CFD and FEM simulations.
| Software | Use Case | Parallel Scaling | GPU Support |
|---|---|---|---|
| ANSYS Fluent | Fluid mechanics, heat transfer, combustion | Excellent (MPI) | Yes (GPU solver) |
| ANSYS Mechanical | Structural analysis, modal, fatigue | Good | Limited |
| OpenFOAM | Open-source CFD; optimization loops | Excellent | In development |
| Abaqus | Nonlinear structural, contact simulation | Good | Limited |
| LS-DYNA | Crash, impact, blast dynamics | Excellent (MPP) | Yes |
| MSC Nastran | Modal analysis, aeroelasticity, certification | Good | No |
| Rocky DEM | Discrete element method (particles) | Very good | Yes (GPU-native) |
Sizing note: A RANS CFD computation with 10–50 million cells typically requires 64–256 MPI cores and 128–512 GB RAM per node. LES or transient workloads can multiply these values several times.
Materials Science and Computational Chemistry
Advanced materials R&D requires a broad computational spectrum, from atomistic to continuum modeling.
| Software | Method | Use Case |
|---|---|---|
| VASP | DFT | New material design, energy band structure, surface chemistry |
| Quantum ESPRESSO | DFT / DFPT | Phonons, vibrational spectroscopy, thermoelectrics |
| LAMMPS | Molecular dynamics | Polymer mechanical properties, interface kinetics |
| GROMACS | Molecular dynamics | Coatings, adhesive formulation, protein-material interaction |
| CP2K | Hybrid DFT/MM | Reaction mechanisms, catalyst design |
| WIEN2k | Full-potential DFT | Electronic structure, magnetic properties |
DFT calculations (VASP, Quantum ESPRESSO) require strong InfiniBand connectivity and high memory. 64–512 cores is a typical range for k-point parallel runs.
Computational Chemistry and Process Simulation
- ORCA / Gaussian: Quantum chemistry, reaction mechanisms, spectral properties; 256 GB–1 TB RAM per node
- COSMO-RS / Aspen Plus: Thermodynamic modeling and process design; small parallel workloads
- OpenCalphad: Phase diagrams, thermodynamic optimization; CPU-intensive
- COMSOL Multiphysics: Multiphysics simulation; large local memory per node is critical
AI-Assisted R&D
Modern R&D centers run hybrid workloads combining simulation data with machine learning:
- Surrogate model training: Fast forward model training from large simulation datasets (PyTorch, TensorFlow)
- Optimization loops: Parameter sweeps via Bayesian optimization or evolutionary algorithms
- ML force fields: Speed-up over DFT using MLIP (MACE, NequIP, DeePMD)
- Image analysis: Deep learning processing of microscopy data (SEM, TEM, XCT)
NVIDIA A100/H100 or L40S-based nodes are recommended for GPU workloads.
R&D HPC Cluster Reference Configuration
Recommended configuration for a medium-sized R&D center:
R&D Center HPC Cluster — Reference Architecture
Login / Pre-Post Nodes (2 units, HA)
├── CPU Compute Nodes (16–48 units)
│ └── 2× AMD EPYC 9654 (96-core), 512 GB DDR5
│ Total: 3,072–9,216 cores
│ Use: CFD, FEM, DFT, MD (CPU-intensive)
│
├── High-Memory Nodes (2–4 units)
│ └── 2× AMD EPYC 9654, 1.5–2 TB DDR5
│ Use: Gaussian, Nastran large modal, large FEM
│
├── GPU Nodes (4–8 units)
│ └── 2× Intel Xeon Gold + 4× NVIDIA H100 80GB SXM5
│ Use: ML force fields, Rocky DEM, ANSYS GPU, deep learning
│
└── Parallel File System
└── BeeGFS 7.4, NVMe metadata + SAS/NVMe data
Capacity: 200 TB–2 PB; Bandwidth: 10–40 GB/s
Network: Mellanox InfiniBand HDR200 (fat-tree topology)
Job Scheduler: SLURM 23.x + project-based accounting
Identity: OpenLDAP + SSH certificate authentication
Monitoring: Prometheus + Grafana; SLURM accounting dashboard
KVKK Compliance and Turkey Location
Product and material formulations developed by R&D centers fall within the scope of trade secrets. Keeping project data in-country is critical both for KVKK compliance and for protecting intellectual property.
The infrastructure Mevasis provides to R&D centers is located in Turkey. Under the managed service scope:
- Computing and storage infrastructure remains physically in Turkey
- Data transfers occur over encrypted channels (WireGuard / IPsec VPN)
- Access control and audit logging are provided as standard
- Infrastructure inventory documentation is available for audits required by R&D Center certification
Typical Workload Profiles and Sizing
| Sector / Use Case | Dominant Software | Recommended Core Range | Memory / Node | Primary Bottleneck |
|---|---|---|---|---|
| Automotive part design (CFD) | Fluent, OpenFOAM | 64–512 | 256–512 GB | MPI communication latency |
| Electronics cooling (CHT) | Fluent, StarCCM+ | 32–256 | 128–256 GB | I/O (large mesh) |
| New material DFT | VASP, QE | 64–512 | 128–512 GB | Memory bandwidth |
| Polymer MD simulation | LAMMPS, GROMACS | 32–256 | 128–256 GB | GPU acceleration |
| Quantum chemistry | ORCA, Gaussian | 32–128 | 256 GB–1 TB | Single-node large memory |
| AI / surrogate model | PyTorch, JAX | 4–16 GPUs | 80 GB VRAM/GPU | GPU bandwidth (NVLink) |
| Crash / impact simulation | LS-DYNA MPP | 64–256 | 256–512 GB | MPI memory |
| Discrete element (DEM) | Rocky DEM | 4–8 GPUs | 40–80 GB VRAM/GPU | GPU cores |
Mevasis R&D HPC Services
Mevasis provides end-to-end support for R&D centers from project initiation through ongoing operations.
Infrastructure Design and Installation
We determine the right cluster size based on your workload profiles and deliver hardware procurement, InfiniBand network design, BeeGFS parallel file system installation, and SLURM configuration as a turnkey solution. See our HPC Infrastructure Setup and Sales service page.
Managed Rental Model
You can rent dedicated computing capacity assigned to your R&D center without fixed capital investment. Monthly or project-based billing; you define your own priority policies on SLURM. See our Dedicated HPC Rental options.
Software Environment and Optimization
Correctly compiling and tuning MPI parameters for ANSYS, LS-DYNA, VASP, GROMACS, and other software can make a 20–50% performance difference per workload. The Mevasis team profiles your software stack and determines the optimal configuration. Details at our HPC Technical Support and Consulting page.
Ongoing Technical Support
Ticket-based support during business hours and extended SLA options for critical workloads. Software updates, SLURM version upgrades, and hardware monitoring are included in the support scope.
Contact us to discuss your R&D HPC needs →
Frequently Asked Questions
Will my existing commercial licenses (ANSYS, LS-DYNA, VASP) work on this infrastructure? Yes. ANSYS HPC pack licenses, LS-DYNA MPP licenses, and VASP site licenses work on Mevasis infrastructure. You can leave your license server at its current location or move it inside the Mevasis infrastructure. Support is provided for MPI tuning compatible with your licensing model.
Is InfiniBand essential for DFT calculations (VASP, Quantum ESPRESSO)? Small systems (small unit cell, few k-points) can run over 25 GbE. For more than 128 cores and intensive k-point parallel runs, InfiniBand HDR200 noticeably reduces MPI communication latency and shortens total solution time by 25–40%. Mevasis provides network recommendations based on your workload profile.
How is KVKK and data security managed for a company with R&D Center status? Mevasis infrastructure is located in Turkey. Access is provided via encrypted VPN channels, audit logs are maintained, and physical security is provided in environments meeting Tier III data center standards. If on-premise installation is preferred, data never leaves your facilities.
What is the difference between choosing Mevasis over cloud HPC (AWS, Azure)? For R&D centers with continuous workload profiles, the cost difference is significant: on-demand cloud core-hour costs range from $0.05–0.09, while managed rental models offer substantially lower rates. In addition, data transfer latencies, license server compatibility, and KVKK requirements provide concrete advantages in favor of on-premise or domestically managed rental.
What storage size is sufficient? As a starting point for a medium-scale R&D cluster, 100–500 TB of high-speed scratch (NVMe-based BeeGFS) plus a slow tier for long-term project archiving (SAS or object storage) is recommended. CFD and DFT workloads generate large temporary files; active scratch requirements can be optimized through checkpoint strategies.