GPU kernel consulting and performance engineering
Performance Consulting
We help engineering teams turn slow, expensive, or underutilized compute pipelines into measured production speedups.
ArrayFire works across CUDA, OpenCL, oneAPI/SYCL, Jetson edge systems, C/C++ kernels, Python pipelines, ArrayFire code, and multi-GPU architectures. The target is not abstract acceleration. It is lower latency, higher throughput, reduced cloud and hardware cost, faster model inference, and better GPU utilization.
Performance explainer
See how we approach acceleration work.
The short version: we start with measurements, isolate the limiting path, and choose the least complicated change that produces a defensible speedup.
Outcomes we optimize for
Make the important path measurably better.
The right answer may be a kernel rewrite, a memory-layout change, a batching strategy, an architecture refactor, or a hard recommendation not to use the GPU for a particular path. We optimize for the business metric that matters.
Lower latency
Reduce response time for real-time inference, CT reconstruction, trading analytics, robotics, edge AI, and interactive image or signal processing.
Higher throughput
Process more frames, policies, simulations, scans, or batches per second by improving parallelism and avoiding host-device stalls.
Lower compute cost
Reduce cloud spend and hardware footprint by getting more useful work out of the accelerators you already pay for.
Better utilization
Find idle GPUs, memory-transfer bottlenecks, serial sections, load imbalance, and kernels that leave performance on the table.
Where we work
Kernel-level help, system-level judgment.
CUDA and C++ kernels
Kernel fusion, memory coalescing, occupancy, streams, shared memory, CUDA Graphs, multi-GPU execution, MPI interaction, and profiling-guided rewrites.
OpenCL and oneAPI/SYCL
Portable accelerator implementations, backend tradeoff analysis, device selection, data movement strategy, and performance parity across hardware ecosystems.
Embedded and Jetson
Edge deployments where power, latency, memory, thermal behavior, camera streams, and on-device inference all compete for the same budget.
Python to production
Moving NumPy, CuPy, PyTorch, JAX, image-processing, and model-inference bottlenecks into tested C++/CUDA or ArrayFire-backed production paths.
What you get
A practical engineering engagement, not a vague acceleration promise.
Performance audit
Profiling runs, bottleneck identification, hardware-fit analysis, and a clear view of whether the GPU is the right lever.
Optimized code
Kernel rewrites, pipeline changes, memory movement fixes, batching strategies, and code your team can keep.
Benchmarks
Before-and-after measurements, reproducible test harnesses, and results tied to latency, throughput, utilization, or cost.
Architecture guidance
Recommendations for CPU/GPU partitioning, multi-GPU scaling, edge deployment, reliability, and future hardware choices.
Technical handoff
Documentation, report-ready findings, code walkthroughs, and training so your team understands the result.
Proof from real projects
We have done this work in production code.
ArrayFire's public case studies and blog archive show the same pattern across finance, defense, medical imaging, retail, and scientific workloads: profile the actual system, identify the constraint, then make the measured path faster.
to
30m
Fortune 300 financial company
Ported C/C++ CPU calculations to CUDA for hedge-fund projection scenarios, reducing overnight runs to roughly 30 minutes and supporting larger multi-GPU clusters.
Read the case studyto
320
Reveal Imaging
Improved CUDA cone-beam reconstruction from about 200 to 320 slices per second, while cleaning up bottlenecks, bugs, compiler issues, and GPU-display constraints.
Read the case studyMatched filtering and backprojection
Demonstrated GPU acceleration for synthetic aperture radar image formation, with Jacket delivering 20-50x over MATLAB CPU and ArrayFire faster still.
Read the blog postFortune 200 insurance company
Profiled a 1,300-core production system, found hidden I/O, database, and load-balancing bottlenecks, and prioritized recommendations that later reduced runtime by nearly 39%.
Read the case studyGlasses.com 3D try-on
Accelerated a CPU-bound rendering path from 30 seconds to about 10 seconds per session, beating the target speed while improving error-handling stability.
Read the case studyAutomated trading workload
Reduced a MATLAB trading-analysis component from 70 minutes to 1 minute, 52 seconds with GPU and multi-GPU execution.
Read the blog postLanguages, platforms, and systems
Bring us the messy performance problem.
We are useful when the answer crosses abstraction layers: algorithm design, memory layout, kernel language, runtime behavior, hardware constraints, testing, deployment, and team handoff.
Start with the bottleneck
Show us the path that is too slow.
We will help you decide whether to profile, port, rewrite, batch, scale out, or leave it alone.
