ReadMeFeed / Community knowledge
Real questions. Useful conversations. Find the people who know your stack.
Frameworks & web · #opencl
Learn how to query CL_HALF_FP_CONFIG, compile with -cl-std=CL2.0, and use half‑precision intrinsics in OpenCL to gain bandwidth and compute benefits on supported GPUs.
Sub‑buffers let you partition a large OpenCL buffer into independent regions, enabling concurrent kernel execution without copying data. This guide covers how to create and use sub‑buffers, common pitfalls, and verification steps.
Learn how OpenCL’s __local memory and work‑group barriers can turn a bandwidth‑bound matrix multiply into a compute‑friendly tiled kernel, with a concrete 16×16 example and practical tuning steps.
Learn when to use CL_MEM_COPY versus CL_MEM_USE_HOST_PTR in OpenCL to eliminate PCIe bottlenecks and implement zero-copy memory transfers on integrated GPUs.
Learn how to replace cv::Mat with cv::UMat to let OpenCV automatically run on CPU, OpenCL, or CUDA backends without changing your algorithm code.
Learn how to allocate, use, and verify fine‑grained SVM buffers in OpenCL 2.0 to share pointers between host and kernel without explicit map/unmap calls.
A technical guide to diagnosing and fixing CL_DEVICE_NOT_FOUND errors in OpenCL, covering ICD loader failures, driver mismatches, and Linux permission issues.
Learn how to use OpenCL Local Memory (LDS) and tiling patterns to reduce global memory latency and improve kernel performance in compute-heavy applications.
Global memory latency is often the real bottleneck in OpenCL kernels. This post explains the tiling pattern with a worked 1D blur kernel, barrier rules, and the occupancy trade-off that decides whether local memory actually helps.