Processing

/diː-ɛs-piː/

n. “A specialized microprocessor designed to efficiently perform digital signal processing tasks.”

DSP, short for Digital Signal Processor, is a type of processor optimized for real-time numerical computations on signals such as audio, video, communications, and sensor data. Unlike general-purpose CPUs, DSPs include specialized hardware features like multiply-accumulate units, circular buffers, and hardware loops to accelerate mathematical operations commonly used in signal processing algorithms.

/ˈoʊpən-siː-ɛl/

n. “An open standard for cross-platform parallel computing on CPUs, GPUs, and other processors.”

OpenCL, short for Open Computing Language, is a framework for writing programs that execute across heterogeneous platforms, including CPUs, GPUs, digital signal processors (DSPs), and other processors. Unlike proprietary solutions like CUDA, OpenCL is vendor-agnostic, enabling developers to target multiple hardware types from a single codebase.

/paɪ-ˈkuː-də/

n. “A Python library that lets developers access CUDA from Python programs.”

PyCUDA is a Python wrapper for NVIDIA CUDA, enabling developers to write high-performance parallel programs for GPUs directly from Python. It combines Python’s ease of use with the computational power of CUDA, allowing rapid development, experimentation, and integration with scientific or AI workflows.

/ˈkuː-də/

n. “A parallel computing platform and programming model for NVIDIA GPUs.”

CUDA, short for Compute Unified Device Architecture, is a proprietary parallel computing platform and application programming interface (API) developed by NVIDIA. It enables software developers to harness the massive parallel processing power of NVIDIA GPUs for general-purpose computing tasks beyond graphics, such as scientific simulations, deep learning, and data analytics.

/ˌdʒiː-piː-dʒiː-piː-juː/

n. “The use of a graphics processing unit to perform general-purpose computation.”

GPGPU, short for General-Purpose computing on Graphics Processing Units, refers to using a GPU to perform computations that are not limited to graphics rendering. While GPUs were originally designed to accelerate drawing pixels and polygons, their massively parallel architecture makes them exceptionally good at handling large-scale numerical and data-parallel workloads.

/ˌdʒiː-piː-ˈjuː/

n. “The processor built for crunching graphics and parallel tasks.”

GPU, short for Graphics Processing Unit, is a specialized processor designed to accelerate rendering of images, video, and animations for display on a computer screen. Beyond graphics, modern GPUs are also used for parallel computation in fields like machine learning, scientific simulations, and cryptocurrency mining.

Key characteristics of GPU include: