Your GPU code is slower than it should be. We find out why — and fix it.
SVACL is a GPU and parallel-computing performance practice. We profile compute-intensive code, diagnose the real bottleneck, and re-engineer it for measurable speedups on modern CPUs, GPUs, and clusters — with the benchmarks to prove it.
Book a free 30-minute consultation write to us at contact@svacl.com
See how we work → services
Sequential code can’t keep up with modern hardware
Moore’s Law has plateaued. Single-thread performance has stopped growing, but your workloads haven’t. Modern hardware has parallelism — dozens of CPU cores, thousands of GPU threads, whole clusters of nodes — but that hardware only pays off if your code is built to use it.
Most isn’t. Scientific simulations, ML pipelines, financial models, and engineering codes routinely run at a fraction of the hardware they sit on: under-occupied GPUs, memory-bound kernels, stalled pipelines, serial bottlenecks that no amount of bigger hardware will fix.
That gap — between what your hardware can do and what your code actually does — is exactly what we close.
What we do
GPU Performance Engineering
We make GPU code fast and prove it. Kernel optimization, memory and occupancy tuning, multi-GPU scaling, and CPU–GPU overlap — driven by Nsight Systems, Nsight Compute, and roofline analysis, not guesswork.
Code Parallelization
We turn sequential C, C++, Fortran, and Python into efficient parallel implementations using CUDA, OpenMP, MPI, SYCL, and hybrid models — with documentation and benchmarks your team can build on.
Performance Audits
A fixed-scope, fixed-price diagnostic of your hottest code path: where the time really goes, why, and a prioritized roadmap of what to fix first. A low-risk way to start.
Training & Knowledge Transfer
Hands-on parallel-programming and GPU-optimization training for your engineers, taught by a practitioner who does this work for a living — not a generic course.
Explore services → services
The Performance Detective approach
Most “optimization” is guesswork — someone changes code that looks slow and hopes. We don’t guess. Every engagement starts the same way: measure, locate, prove.
Measure — profile the real workload on real hardware to see where time and memory actually go.
Locate — identify the true bottleneck (it’s usually not where people think), backed by roofline and occupancy data.
Prove — fix it, then benchmark before-and-after against your reference output so the speedup is a number, not a claim.
You get faster code and the evidence that it’s faster.
Led by a specialist, not an agency
Led by Dr. Mandar Gurav — PhD, IIT Bombay ·Ex-Intel, Nvidia · 16+ years in CUDA, OpenMP, MPI, and GPU performance engineering.
Engineers and researchers trained at IITs, National Labs, Industry, and different academic/research institutes.
About the founder → about
Start with a free resource
Before you hire anyone, grab one of our field guides — the same checklists we use on real engagements:
- Nsight Compute Triage Playbook — read a GPU profile and find the bottleneck fast.
- GPU Roofline Playbook — know whether your kernel is compute- or memory-bound, and what to do about it.
- GPU Porting Scorecard — decide if a workload is worth moving to the GPU before you spend the effort.
- CUDA Bug Field Guide — the failure patterns that cost teams the most time, and how to spot them.
Get the playbooks → resources
Have a slow kernel, a stalled GPU port, or a code that won’t scale?
Tell us what you’re working on. The first call is free, 30 minutes, and ends with a straight answer about whether we can help and how.
Book a free consultation → write to us at contact@svacl.com