---
title: "From Prototype to Performance: Optimizing OpenCV Resize on Volcanic GPUs"
description: Explore practical techniques for optimizing OpenCV resize operations on Volcanic GPUs, focusing on correctness, performance, and insightful iteration strategies.
---

[Imagination Blog](https://blog.imaginationtech.com)

# [From Prototype to Performance: Optimizing OpenCV Resize on Volcanic GPUs](https://blog.imaginationtech.com/from-prototype-to-performance-optimizing-opencv-resize-on-volcanic-gpus)

 Written by [Mario Florea](https://blog.imaginationtech.com/author/mario-florea) | Oct 7, 2026, 8:00:00 AM

This is a practitioner-oriented prototype case study. It focuses on implementation and optimization technique rather than product claims, benchmark marketing, or formal KPI reporting.

## Why this case study matters

Image resize is a useful workload for GPU optimization work because it is easy to understand, common in computer vision pipelines, and sensitive to memory access patterns. That makes it a strong teaching example. The operation itself is straightforward, but good performance depends on practical engineering choices such as data layout, sampler usage, work-item structure, validation flow, and profiling method.

The goal in this exercise was simple:

- Create a reference implementation path
- Build a functionally correct GPU implementation
- Iterate toward better performance on Volcanic-class GPU hardware

> The workflow described here is broadly useful beyond resize. The same pattern applies to many compute kernels: understand the math, establish correctness, instrument the execution path, then optimize one bottleneck at a time.

## Problem setup

The workload is OpenCV-style resize, using linear interpolation. Instead of starting with a CPU implementation and then porting it, the work moved directly to OpenCL because the resize mathematics are well understood and there is already an open-source OpenCL reference path available in OpenCV.

That gave two immediate advantages:

- A known-good baseline for validating numerical correctness
- A comparison point for assessing optimization ideas

For rapid iteration, execution was orchestrated from Python while the compute kernel lived in OpenCL C.

## Development flow

The work was structured around two files:

1. `main.py` for orchestration, compilation, launch configuration, validation, and profiling
2. `resize.cl` for kernel logic and optimization experiments

This separation is small but powerful. It lets you change launch parameters, compiler flags, repeat counts, and test scales without constantly rebuilding a larger application.

> **What a practical iteration loop looks like:**
> 
> A typical cycle is: change kernel logic, run the Python driver, validate output against OpenCV, collect timing data, inspect trends, then repeat. This keeps correctness and performance tightly coupled throughout development rather than treating validation as an afterthought.

## Step 1: Build a reliable reference path

Before optimization, the resize operation needs to be understood mathematically. For linear interpolation, the key idea is mapping each output pixel back into the input image and sampling at the corresponding coordinate.

In this case, OpenCV’s existing behavior served as the functional reference. That matters because “looks correct” is not enough for image processing. Small rounding or coordinate mistakes can propagate into visible artifacts or silent numeric drift.

> Best practice: pick a trusted reference implementation early and keep it in the loop throughout optimization. Performance work is much faster when every experiment can be checked automatically.

## Step 2: Make the GPU version correct first

The first milestone was not speed. It was correctness. Using OpenCL image objects and samplers made the initial implementation simpler, especially because linear interpolation could be delegated to the sampling hardware.

At this stage, the important outcome was that GPU results could be compared directly with OpenCV output. The validation path computed a per-pixel absolute difference and enforced a very small tolerance to account for rounding behavior.

| 1 | output\_ref = cv.resize( |
| --- | --- |
| 2 | input, |
| 3 | (int(input\_size\_x \* SCALE), int(input\_size\_y \* SCALE)), |
| 4 | interpolation=cv.INTER\_LINEAR, |
| 5 | ) |
| 6 |  |
| 7 | diff\_image = np.abs(output.astype(np.int16) - output\_ref.astype(np.int16)) |
| 8 | max\_diff = np.max(diff\_image) |
| 9 |  |
| 10 | if max\_diff \> 1: |
| 11 | raise RuntimeError(f"Output is invalid, diff: {max\_diff}") |

 

 This is the kind of guardrail that keeps optimization honest. If performance improves but validation breaks, the change is not done.

 

## Step 3: Optimize the host-side execution path

Not all optimization happens in the kernel. The Python driver can materially affect iteration speed and kernel performance.

### Useful compiler options

Several compile-time options were highlighted as good defaults for this type of workload:

- `-cl-fast-relaxed-math` to allow more aggressive math optimization. In practice, this is useful for image-processing kernels because it lets the compiler use faster floating-point sequences, including relaxed rounding, algebraic simplifications, reciprocal-based division, and fused multiply-add style instructions where available. The tradeoff is that results may no longer be strictly IEEE-exact: tiny rounding differences are expected, and edge cases such as `NaN`, infinities, or denormal values may not be handled the same way as a strict reference path. For resize and similar vision workloads, this is often a good trade when validation allows a small tolerance and visual quality is not affected.
- `-cl-uniform-work-group-size` when the work-group shape is known and stable. This tells the compiler that every work-group has the same local dimensions, which can remove overhead related to handling partial or non-uniform groups. The main tradeoff is flexibility: the host code must choose global sizes that are evenly divisible by the local size in every dimension, or pad the problem so the final tiles are complete. It is a good fit for fixed-size tiled kernels, predictable image dimensions, or pipelines where padding and explicit boundary handling are already part of the design.
- `-unroll-threshold 10000` to encourage more loop unrolling where beneficial. Raising this threshold gives the optimizer more room to expand small loops, which can expose consecutive memory accesses, reduce branch overhead, and help the compiler combine scalar operations into more efficient instruction sequences. The downside is that unrolling can increase code size, register pressure, and instruction-cache pressure. If register use rises too much, occupancy can fall or spills can appear, making the kernel slower. This option is most useful for small, fixed-trip-count loops, such as processing a short group of pixels per work-item or applying a compact stencil/filter.

These options do not replace kernel design, but they can help the compiler generate better code once the kernel structure is sensible.

### Global and local size selection

The case study also emphasizes launch configuration as an optimization layer. One approach grouped work so that a single work-item handled multiple pixels. This reduced total work-item count and created opportunities for more efficient access behavior.

| 1 | GROUP = 4 |
| --- | --- |
| 2 | GLOBAL\_GROUP\_SIZE = (output\_size\_x \* output\_size\_y // GROUP, 1, 1) |
| 3 | LOCAL\_GROUP\_SIZE = None |

 

 

 

 

 

 

Leaving local size unspecified can be a reasonable experimental choice when the runtime is doing a good job and the global range is flexible enough to permit efficient scheduling.

### Measure performance carefully

Profiling used OpenCL event timing across repeated runs, with outlier trimming when enough samples were available. This is a practical technique for reducing noise and comparing kernel ideas more reliably.

| 1 | times = sorted(times) |
| --- | --- |
| 2 | if REPEATS \>= 10: |
| 3 | times = times\[len(times) // 10 : -(len(times) // 10)\] |
| 4 |  |
| 5 | avg\_time\_s = np.average(times) |
| 6 | useful\_mem\_bytes = input.nbytes + output.nbytes |

 

 

In addition to runtime, the workflow tracked useful memory moved and derived useful bandwidth. That gives a clearer picture of whether an optimization is actually improving data movement efficiency.

> A strong optimization harness does three things well: launches quickly, validates automatically, and produces stable measurements.

## Kernel evolution

The most instructive part of the case study is the kernel iteration process. The final value is not just the best version found so far, but the reasoning behind each step.

### Baseline: simple 1:1 copy behavior

The first kernel used a very simple mapping from input to output. This was not meant to be the best possible memory copy. It existed to verify the Python driver, test image object usage, confirm indexing assumptions, and establish a rough sense of achievable throughput under simple conditions.

| 1 | const sampler\_t sampler = CLK\_NORMALIZED\_COORDS\_FALSE \| CLK\_ADDRESS\_NONE \| CLK\_FILTER\_NEAREST; |
| --- | --- |
| 2 |  |
| 3 | kernel void resize(image2d\_t read\_only input, const int output\_size\_x, global uint4 output\[\]) { |
| 4 | const int2 pixel\_coord = {get\_global\_id(1), get\_global\_id(0)}; |
| 5 | output\[output\_size\_x \* pixel\_coord.y + pixel\_coord.x\] = read\_imageui(input, sampler, pixel\_coord); |
| 6 | } |

 

 

Even a basic kernel like this is useful because it creates a reference point for later complexity. If every new version performs worse, that tells you something important.

### Naive resize with linear sampling

The first real resize implementation used a linear sampler and mapped output coordinates back to input coordinates. This made the algorithm compact and easy to reason about.

| 1 | const sampler\_t sampler = CLK\_NORMALIZED\_COORDS\_FALSE \| CLK\_ADDRESS\_NONE \| CLK\_FILTER\_LINEAR; |
| --- | --- |
| 2 |  |
| 3 | kernel void resize( |
| 4 | read\_only image2d\_t input, |
| 5 | const float inv\_scale, |
| 6 | const int output\_size\_x, |
| 7 | global char output\[\]) |
| 8 | { |
| 9 | const int2 out\_pixel\_coord = {get\_global\_id(0), get\_global\_id(1)}; |
| 10 | const int out\_idx = output\_size\_x \* out\_pixel\_coord.y + out\_pixel\_coord.x; |
| 11 |  |
| 12 | const float2 in\_coords = (convert\_float2(out\_pixel\_coord) + 0.5F) \* inv\_scale; |
| 13 |  |
| 14 | output\[out\_idx\] = read\_imageui(input, sampler, in\_coords).x; |
| 15 | } |

 

This version was correct, but performance dropped sharply versus the simpler copy-style kernel. That is a normal and valuable finding. Correctness is often easy to reach before efficiency is.

The likely issue was inefficient burst behavior for reads and writes, which limited effective bandwidth.

### Grouped-pixel processing

A stronger version grouped pixels in batches of four per work-item. This changed the execution shape in a few useful ways:

- Reduced work-item count
- Made reads and writes easier to coalesce
- Improved the chance of getting more value from very fast local texture cache behavior
- Created opportunities for the compiler to emit more efficient stores

| 1 | const sampler\_t sampler = CLK\_NORMALIZED\_COORDS\_FALSE \| CLK\_ADDRESS\_NONE \| CLK\_FILTER\_LINEAR; |
| --- | --- |
| 2 |  |
| 3 | kernel void resize( |
| 4 | read\_only image2d\_t input, const float inv\_scale, const int output\_size\_x, |
| 5 | global char output\[\]) { |
| 6 | const int gid = get\_global\_id(0) \* GROUP; |
| 7 | const int out\_image\_width = get\_image\_width(input) / inv\_scale; |
| 8 |  |
| 9 | for (int i = 0; i \< GROUP; ++i) { |
| 10 | const int2 out\_pixel\_coord = { |
| 11 | gid % out\_image\_width + i, |
| 12 | gid / out\_image\_width, |
| 13 | }; |
| 14 |  |
| 15 | const float2 in\_coords = (convert\_float2(out\_pixel\_coord) + 0.5F) \* inv\_scale; |
| 16 | const char x = read\_imageui(input, sampler, in\_coords).x; |
| 17 |  |
| 18 | output\[gid + i\] = x; |
| 19 | } |
| 20 | } |

 

 

This produced a substantial improvement over the naive resize approach and illustrates a common GPU lesson: changing how work is grouped can matter as much as changing the arithmetic itself.

## What this teaches

Several broader lessons come out of this work:

- **Start with a trusted reference.** Optimization without automatic validation is risky and slow.
- **Keep the host harness lightweight.** Fast iteration changes the pace of engineering work.
- **Measure repeatedly.** Single-run timing is rarely enough to judge a kernel.
- **Expect the first correct version to be slow.** That is part of the process, not a failure.
- **Memory behavior often dominates.** For image workloads, access patterns can outweigh nominal arithmetic simplicity.

## Ideas for further experimentation

The case study also identified promising next steps:

- Try 2D tiling because texture hardware may prefer access patterns closer to Z-order locality
- Tune group size based on scale factor, since upscaling and downscaling can stress the system differently
- Reduce integer-heavy index calculations where possible, especially if they map to lower-throughput execution resources

> **Why these ideas are worth testing:** These are not random tweaks. They each target a plausible bottleneck: locality, workload shape, or instruction mix. Good optimization work is hypothesis-driven. Each experiment should be connected to a specific reason performance may improve.

## Practical takeaway for GPU developers

If you are bringing a vision or image-processing kernel onto a GPU, this resize example is a useful model for how to proceed. Build a small driver that can compile, launch, validate, and profile. Use an accepted software reference for correctness. Begin with the most direct kernel you can write. Then improve the execution pattern step by step, measuring after every change.  The result is not just a faster kernel. It is a repeatable engineering method.

References

- [OpenCV: Resizing and Rescaling Images with OpenCV](https://opencv.org/blog/resizing-and-rescaling-images-with-opencv/)
- [LearnOpenCV: Image Resizing with OpenCV](https://learnopencv.com/image-resizing-with-opencv/)
- [OpenCV resize.cpp source](https://github.com/opencv/opencv/blob/4.x/modules/imgproc/src/resize.cpp)
- [OpenCV OpenCL overview](https://opencv.org/opencl/)
- [OpenCV OpenCL resize kernel](https://github.com/opencv/opencv/blob/4.x/modules/imgproc/src/opencl/resize.cl)
- [Khronos OpenCL compiler options](https://registry.khronos.org/OpenCL/specs/3.0-unified/html/OpenCL_API.html#compiler-options)

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