GPUohjaukselle

GPUohjaukselle is a Finnish-developed software framework designed to simplify the utilization of graphics processing units (GPUs) for general-purpose computing tasks. The project was initiated in 2014 by a collaborative team from the University of Helsinki and several local tech companies to provide an open‑source, cross‑platform API that bridges the gap between high‑level programming languages and low‑level GPU hardware interfaces. The framework supports both CUDA and OpenCL, enabling developers to write code in C++, Python, and JavaScript that can target NVIDIA, AMD, and Intel GPUs without having to manage device-specific details.

The core architecture of GPUohjaukselle consists of three layers: an abstraction layer that exposes intuitive function calls, a command scheduler that handles concurrent queue execution, and a memory management module that abstracts buffer allocation and transfer between host and device. Users can allocate memory buffers with automatic alignment, launch kernels by specifying grid and block dimensions, and synchronize independently on multiple streams. The framework also includes a profiling API that records kernel execution times, memory bandwidth, and power consumption, allowing developers to optimize performance on heterogeneous systems.

GPUohjaukselle is primarily used in scientific computing, machine learning, image processing, and real‑time graphics research. It is distributed under the MIT license, hosted on GitHub, and actively maintained with regular releases that incorporate new GPU architectures and driver updates. The project encourages community contributions through pull requests, issue tracking, and a yearly code‑review sprint.

While GPUohjaukselle abstracts many of the complexities of GPU programming, experts note that deep understanding of memory hierarchies, warp divergence, and device synchronisation remains essential for achieving peak performance. Nonetheless, the framework has been adopted by several research labs in Finland for prototyping accelerated algorithms and is cited in multiple peer‑reviewed publications on GPU‑accelerated algorithms.