HPC consultancy

High-performance computing for engineering simulation

Architecture, parallel-performance and deployment advice for organisations running CFD, multiphysics, optimisation and scientific-computing workloads—focused on measurable throughput, scalability and cost efficiency.

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Simulation-led HPC decisions

Size the computing system around the real workload

Peak specifications do not determine simulation performance. Mesh size, memory access, sparse linear algebra, inter-process communication, storage behaviour, GPU suitability and solver scaling must be evaluated together.

LIKUA combines hands-on parallel solver development with production CFD and multiphysics workloads. Recommendations are based on how the application actually uses the machine, not on generic benchmark numbers.

Focused capabilities

HPC consultancy for numerical engineering

01

Architecture planning

Balanced workstation, server and cluster configurations based on target applications, case sizes, concurrency and budget.

  • CPU and GPU suitability
  • Memory capacity and bandwidth
  • Interconnect and topology planning
  • NVMe, scratch and archive strategy
02

Performance diagnosis

Measurement-led identification of compute, memory, communication, I/O and algorithmic bottlenecks.

  • Strong- and weak-scaling studies
  • CPU, memory and communication profiling
  • Solver and preconditioner timing analysis
  • Cost per case and throughput assessment
03

Parallel software optimisation

Improvement of distributed-memory scientific software and irregular-mesh workflows.

  • MPI and hybrid parallelisation strategy
  • Domain decomposition and load balance
  • Communication reduction and data layout
  • Parallel linear-solver integration
04

GPU readiness and acceleration

Evidence-based assessment of which kernels and solver stages can benefit from GPU execution.

  • GPU memory and precision requirements
  • CPU–GPU transfer and residency analysis
  • CUDA-aware numerical-library configuration
  • Single- and multi-GPU scaling strategy
05

Scientific software environment

Reproducible build and runtime configurations for demanding numerical applications.

  • Compiler and numerical-library selection
  • MPI, sparse-solver and GPU-library builds
  • Dependency and version compatibility
  • Repeatable deployment documentation
06

Simulation workflow design

Practical workflows for large case sets, optimisation loops and remote execution.

  • Batch and parameter-study automation
  • Job scheduling and resource requests
  • Checkpoint, restart and data-retention strategy
  • Result transfer and post-processing flow
Workload coverage

Designed for simulation-heavy engineering

CFD Compressible, incompressible, reacting and multiphase flow solvers.
Multiphysics Thermal coupling, sparse systems and mixed computational stages.
Optimisation Concurrent design evaluations, sweeps and automated engineering loops.
Machine learning Physics-informed and graph-based training workloads with high memory demand.
Decision criteria

Every recommendation must improve a measurable outcome

Time to solution

Reduce elapsed time for the single critical case, including solver and I/O behaviour.

Engineering throughput

Increase completed, usable simulations per day across teams and design campaigns.

Total cost

Balance acquisition, energy, software effort and under-used resources over the system life.

Deliverables

A procurement or optimisation decision supported by data

Architecture study

  • Workload and growth assessment
  • Candidate architecture comparison
  • Memory, storage and network sizing
  • Expected suitability by application type
  • Procurement specification and vendor-neutral evaluation criteria

Performance-improvement study

  • Reproducible baseline and benchmark matrix
  • Scaling curves and bottleneck evidence
  • Configuration or code-level recommendations
  • Before-and-after performance comparison
  • Deployment notes and repeatable operating parameters
Workflow

Measure first, then select or optimise

Characterise Applications, case sizes, concurrency, precision, memory and future growth.
Baseline Representative runtime, resource use, scaling and reliability measurements.
Diagnose Compute, memory, communication, I/O and algorithmic bottlenecks.
Evaluate Architecture or optimisation options against the same benchmark set.
Recommend Documented configuration, expected benefit, limits and implementation path.

Do not buy peak specifications. Buy performance for the workloads you run.

Share representative case sizes, current runtimes, software stack, concurrency needs and the systems under consideration. LIKUA can define a benchmark-led architecture or performance study.

Discuss your HPC requirement