Case-Study: V-Nova - GPU Porting from OpenCL to Metal
Dec 15, 2023
Case study on moving a GPU application from OpenCL to Metal for our client V-Nova.
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GPU Performance Engineering
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Why Choose Us?
We Profile First, Then Make It Fast
Most GPU work handed to us starts with the wrong assumption about where the time goes. We measure the workload honestly, then change the algorithm, the memory layout, or the kernel, whichever actually moves the number.
Founder-Led GPU Expertise
Balázs Keszthelyi built the first OpenCL benchmark adopted by major GPU vendors and architected the VC-6 codec. With decades of GPU-first innovation, TechnoLynx is led by one of the field’s most credible pioneers.
Algorithm Redesign for Speed
We don’t just move code to GPUs, we rethink the algorithm. From simulation engines to custom AI pipelines, we redesign logic to unlock real-world speedups that straight-up GPU porting can’t deliver.
Full-Stack Performance Tuning
GPU speed means nothing if the rest of your stack lags. We optimise across CPU, memory, and I/O to eliminate bottlenecks and ensure your system performs as a whole.
AI + GPU: Smarter, Faster Systems
We blend custom-coded logic with AI inference, optimised for GPU performance engineering. The result: intelligent systems that are fast, efficient, and ready for real-time deployment.
Cross-Platform GPU Porting
From CUDA to Metal, OpenCL to Vulkan: we make your code run fast on any GPU. We’ve helped clients unlock Apple silicon, AMD, and NVIDIA platforms with precision.
Visual Computing, Not Just Compute
We don’t just accelerate code, we visualise it. From GPU-accelerated simulations to 3D rendering and XR, we bring deep graphics expertise to projects that need both performance and visual clarity.
Area of Expertise
GPU Algorithm Redesign
Full-Stack Performance Tuning
Cross-Platform GPU Porting
Real-Time Rendering
AI-Accelerated Visual Computing
XR Simulation & 3D Graphics
Technology Stack
CUDA
OpenCL
Vulkan
Metal
DirectX
TensorRT
ONNX
SYCL
HIP
DX12
WebCompute
WebGL
Want This as a Packaged Engagement?
When the goal is a measured cost-per-request or latency saving on a workload already running in production, the packaged way to buy this is the Inference Cost-Cut Pack: an Audit that ranks where the cost leaks, then an Optimisation Sprint that builds the high-confidence changes and hands back a harness that reproduces the numbers.
Where This Sits
This page is engineering delivered to a client's spec; LynxBenchAI is the open methodology behind how we hold those numbers to a standard in the first place, sustained throughput, per precision, under a declared and bounded optimisation budget. Building the benchmark harness yourself is LynxBenchAI territory; the packaged outcome for your workload is the Inference Cost-Cut Pack above.
Client Testimonials
TechnoLynx delivered the project on time and provided quality outputs that met the client's expectations. The team was proactive in providing ideas and suggestions, and they were careful at properly planning the tasks. The client also praised the team's expertise in GPU programming and AI.
TechnoLynx's skill in low-level software development was impressive. TechnoLynx was able to create four prototypes with common components and an interface for easy maintenance. The client was extremely happy with the solution's speed. Moreover, their communication was seamless and straightforward.
TechnoLynx's unique aspect is that they're able to transform complex theories into practicable and applicable results. TechnoLynx provides research reports and architecture planning documents. The team is able to transform complex theories into practicable and applicable results. TechnoLynx's project management is strong and delivers work on time without hardware issues, being responsive through virtual meetings.
I’m delighted with our collaboration with their team. Thanks to TechnoLynx's work, the client has been able to co-author two patents. They lead responsive project management to solve problems quickly. The team also praises their skilled and knowledgeable team.
We had high-efficiency meetings. TechnoLynx’s work resulted in a successful breakthrough, and their input improved the client’s app. Their flexible and organised project management cultivated a healthy collaboration experience. Ultimately, their professionalism and commitment were impressive.
Frequently Asked Questions
What makes TechnoLynx unique in GPU performance engineering?
TechnoLynx stands out through pioneer-level leadership and proprietary innovation. Our founder, Balázs Keszthelyi, architected the VC-6 codec and built the first OpenCL benchmark adopted by major GPU vendors. This deep-tech heritage allows us to solve optimization challenges that standard engineering firms cannot.
Which GPU hardware and frameworks does TechnoLynx optimize?
TechnoLynx provides cross-platform optimization for a wide range of hardware and frameworks, including:
- Frameworks: CUDA, OpenCL, Vulkan, Metal, and DirectX.
- Hardware: NVIDIA, AMD, Apple Silicon, and edge devices.
We ensure high-performance execution for AI and computer vision pipelines regardless of the underlying architecture.
How does TechnoLynx accelerate AI models for real-time inference?
We achieve low-latency AI inference through three core techniques: 1. Model Quantisation: Reducing precision without losing accuracy. 2. Pruning: Removing redundant parameters. 3. Custom Pipelines: Utilizing TensorRT and ONNX for maximum throughput on specific GPU architectures.
Does TechnoLynx support cross-platform GPU deployment?
Yes, TechnoLynx designs future-proof, vendor-agnostic solutions. We ensure your GPU software is portable across different vendors (e.g., migrating from NVIDIA to AMD) and operating systems, providing long-term flexibility and scalability.
Does TechnoLynx develop GPU-accelerated XR (AR/VR/MR) applications?
Yes, our XR services include:
- GPU-accelerated high-fidelity rendering.
- Real-time tracking optimization.
- Immersive simulation and digital twins for industrial training.
Does TechnoLynx optimize both AI training and inference?
Yes, we tune pipelines for the entire AI lifecycle. This includes multi-GPU setups for scalable training and using optimized runtimes like TensorRT for efficient deployment and inference.
How does TechnoLynx handle multi-GPU and distributed systems?
We architect distributed solutions that achieve near-linear scaling. By optimizing inter-GPU communication, we enable high throughput for large-scale simulations, massive rendering tasks, and complex AI models.
How does TechnoLynx optimize GPU memory and bandwidth?
We treat memory as a primary resource. Using advanced profiling, we analyze memory access patterns to minimize latency and eliminate bottlenecks, which is critical for processing large datasets in real-time.
Does TechnoLynx offer GPU benchmarking and auditing?
Yes. Our auditing services include kernel profiling, shader analysis, and end-to-end system stress testing. We provide clients with actionable data and recommendations to maximize their hardware investment. The measurement discipline behind those numbers, performance read as a property of the whole hardware-and-software stack, sustained under load, reported per precision, is the methodology we develop in the open at LynxBenchAI.
When does algorithmic restructuring beat low-level kernel tuning?
Most of the time, once a workload is profiled honestly. Micro-optimisations rarely beat changing the algorithm or the data layout, because the biggest GPU gains usually come from removing whole memory round-trips or restructuring the dependency graph rather than from squeezing a few percent out of a single kernel. We always profile first to identify which level of leverage actually applies. See how to profile GPU kernels and when algorithmic restructuring beats kernel tuning.
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Show all 360 deep dives
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- Dell Switch N2224X-ON Review: Network Fabric for GPU Compute Clusters
- Dell X4012 Networking Switch: What It Is and Where It Fits in a GPU Compute Cluster
- DGX Spark Performance: What It Means for Local AI Inference Workloads
- DGX Spark Performance Tests: What the Benchmarks Mean for AI Workloads
- DGX vs HGX: Choosing an NVIDIA GPU Platform for Portable Workloads
- Direct Attach Copper (DAC) Cabling in GPU Simulation Clusters: How It Works
- Direct Attach Copper (DAC) Cables in Tethered XR and GPU Interconnect
- Direct Attach Copper (DAC) Cables: How They Work in XR/GPU Deployments
- Direct Attach Copper (DAC) Cables in Tethered XR: Host-to-Headset Interconnect
- DirectCompute Explained: GPU Compute Shaders for XR Workloads
- Does the SN7100 Have DRAM? On-Device Memory for AR Try-On Rendering
- Download OpenCL: How to Set Up OpenCL for Real-Time GPU Compute Pipelines
- Standalone vs PC-Tethered VR Headsets for Clinical Therapy and Training
- Eagle Supercomputer Explained: GPU-Scale Compute Behind Automotive AR
- EPYC 4005 Motherboard Selection for Edge AR/VR Rendering Nodes
- FFmpeg AVX-512 Performance on AMD Ryzen: CPU-Side Asset Encoding for AR Ads
- CUDA Applications: Where GPU Acceleration Fits in Edge-Bound Agent Inference
- GCC Flags for Edge Inference Builds: What Each Flag Actually Does
- What Is OpenCL? A Practical Guide for Cross-Platform Edge Inference
- Model Optimization for Edge Inference: Distillation, Quantisation, and Runtime Fit
- GCC Optimization Flags Explained: -O2, -O3, -march and What They Mean for a Port
- Logit vs Sigmoid: What the Numerics Mean for a Serving-Path Port
- PoCL Explained: Portable OpenCL as a Porting-Assessment Target Runtime
- Can Machines Make You a Millionaire? AI in Fintech
- Banking Beyond Boundaries: Where AI Actually Earns Its Keep
- The Impact of Conversational AI on the Insurance Industry
- GPU Acceleration for Quantitative Finance Workloads
- DAC Networking for Multi-GPU Quant Finance Clusters
- Imagination GPU Architecture: What It Means for Quant Compute Pipelines
- The OpenCL SDK Explained: Portable GPU Acceleration for Finance Workloads
- Hardware Acceleration Discord: When GPU and CPU Stages Disagree in Video Pipelines
- DACs Networking Explained: Direct Attach Copper in GPU Video-Analytics Fabrics
- DeepSeek on H100: What GPU/CPU Stage Economics Mean for Inference Pipelines
- How to Install OpenCL on Ubuntu for GPU Video-Analytics Workloads
- Install OpenCL on Ubuntu: Setup for GPU/CPU Video Pipelines
- POCL Explained: Portable OpenCL and Where It Fits GPU Video Analytics
- Raspberry Pi TPU: How Edge Accelerators Handle the CPU-Accelerator Handoff
- x265 Encoder: How It Works and What It Means for Analytics Pipelines
- x265 Encoder Software: Where CPU Encoding Fits in a GPU Analytics Pipeline
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- Computer VisionProduction CV pipelines from edge perception to cloud analytics.
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