We don’t believe in one-size-fits-all optimization. Every engagement begins with profiling real code on real hardware — because the bottleneck is almost never where people assume it is. From there, we design and implement a parallelization or optimization strategy that balances raw performance against maintainability, so your team can keep developing the code after we hand it back.
GPU Performance Engineering
Make your GPU code as fast as the hardware allows — and prove it with benchmarks. We work primarily in CUDA, with OpenACC, SYCL, OpenMP Offload, OpenCL, and HIP as the workload demands.
What this covers:
- Kernel development and optimization
- Memory hierarchy tuning — coalescing, shared-memory use, register pressure
- Occupancy and launch-configuration tuning
- Host–device transfer optimization and compute/transfer overlap
- Multi-GPU implementations and scaling
- CPU–GPU hybrid execution
- Performance portability across NVIDIA, AMD, and Intel GPUs
Tools: Nsight Systems, Nsight Compute, roofline analysis, and a current Blackwell-class test environment running recent CUDA toolkits.
Deliverables: optimized source with documentation, before/after benchmarks against your reference output, and a technical report on what was changed and why.
Code Parallelization
Transform sequential applications into high-performance parallel implementations. We analyze your codebase, identify what can be parallelized (and what can’t), and implement it using the right model for the job — CUDA, OpenMP, MPI, SYCL, or hybrid combinations like MPI+OpenMP or MPI+CUDA.
Deliverables:
- Parallelized source code with comprehensive documentation
- Performance benchmarks demonstrating the speedup achieved
- A technical report detailing the strategy used
- Knowledge-transfer sessions for your development team
Performance Optimization & Profiling
Already parallel, but not hitting the performance you expected? We run deep profiling to find what’s actually holding it back — memory access patterns, load imbalance, communication overhead, false sharing, cache behavior — then implement targeted fixes.
Includes:
- Detailed profiling and bottleneck analysis
- Cache and memory-access-pattern optimization
- Load balancing and work-distribution tuning
- Communication-overhead reduction
- Algorithm redesign for better parallelism
- Vectorization and SIMD optimization
Scalability Analysis & Tuning
Make sure your application scales from a workstation to a cluster instead of flattening out at 8 cores. We run formal scaling studies, find where scaling breaks, and fix it.
Includes:
- Strong- and weak-scaling studies
- Amdahl’s- and Gustafson’s-law-based scaling predictions
- Communication-pattern analysis
- I/O bottleneck identification
- Recommendations for optimal resource utilization on your target hardware
Legacy Code Modernization
Bring aging scientific and engineering codes onto modern hardware without breaking them. We modernize serial Fortran/C/C++ applications, introduce current parallel paradigms, and validate every step against the original results so numerical accuracy is preserved.
Includes:
- Refactoring for parallelism
- Migration off deprecated libraries and standards
- Adoption of modern language and toolchain standards
- Validation against legacy reference results
- Documentation and team training
Consulting & Advisory
Need direction before you commit engineering effort? We provide:
- Hardware and architecture selection (which GPU, how many, on-prem vs cloud)
- Parallel-algorithm design and feasibility studies
- Code reviews and best-practice assessments
- Workshops tailored to your team and domain
- Ongoing support and maintenance arrangements
Our process
- Initial consultation — We discuss your code, your performance goals, and your constraints, and give an honest read on feasibility. Free, 30 minutes.
- Code analysis & profiling — We profile the real workload to locate bottlenecks and parallelization opportunities.
- Strategy & proposal — A written proposal: recommended approach, realistic expected speedup, timeline, and cost.
- Implementation — Systematic parallelization and optimization with regular progress updates and intermediate deliverables.
- Testing & validation — Rigorous correctness testing and benchmarking, validated against your original results.
- Delivery & knowledge transfer — Full handover with documentation, performance reports, and a working session for your team.
- Ongoing support (optional) — Post-delivery support, maintenance, and further optimization as your needs grow.
Engagement models
- Performance Audit — Fixed scope, fixed price. The lowest-risk way to start.
- Project engagement — A defined parallelization or optimization project with agreed deliverables and timeline.
- Retainer / ongoing — Continuous optimization or periodic performance audits for teams shipping performance-critical code.
All work can be done under NDA. We work primarily remotely and serve clients globally.
To book a free consultation, write to us at contact@svacl.com
Frequently asked questions
What kinds of applications benefit from this?
Anything that spends real time in computation — scientific simulations, ML training and inference, data analysis, financial modeling, image and signal processing, CFD, molecular dynamics, and similar engineering workloads. If your code spends most of its time in loops over large data, it’s a candidate.
How much speedup can I expect?
It depends on your code, the available parallelism, and the target hardware. Well-suited applications often see 5–15× on multi-core CPUs and substantially more with GPU acceleration. We give a realistic estimate only after profiling — not before.
Will parallelization change my results?
Properly implemented parallel code preserves numerical accuracy within acceptable tolerances. We validate optimized code against your original results and can maintain deterministic behavior where you need it.
Do you work under NDA on proprietary code?
Yes, routinely. Your IP and confidential information are fully protected.
Can you train our team instead of (or alongside) doing the work?
Yes. Many clients combine an engagement with hands-on training so the team can maintain and extend the optimized code.
Remote or on-site?
Primarily remote, which lets us serve clients globally.