BEND

The BEND programming language is a new language designed for high-performance parallel computing, particularly targeting GPU (Graphics Processing Unit) environments. It aims to simplify the development process for applications that require intensive parallel processing, such as those in scientific computing, simulations, and real-time data processing. BEND’s syntax and design are influenced by Python, making it more accessible to developers who are familiar with Python but need the performance benefits of GPU acceleration.

BEND allows developers to write code that can be efficiently executed on GPUs, leveraging their massive parallelism capabilities. This is crucial for tasks that involve large-scale data computations or complex mathematical operations. By using BEND, developers can achieve significant performance improvements compared to traditional CPU-based processing, where parallelism is limited by the number of cores.

For example, in a simulation involving millions of particles, a CPU might process each particle sequentially or in limited parallel batches, which can be slow. In contrast, using BEND on a GPU allows for all particles to be processed simultaneously, drastically reducing computation time and increasing the simulation’s responsiveness and accuracy.

This focus on parallelism and performance makes BEND a valuable tool for developers in fields that demand high computational power, such as gaming, scientific research, and real-time data analytics​.

We are very excited for this as building CUDA programs for the GPU is a minefield and basically an Artform.

 

https://github.com/HigherOrderCO/Bend

 

CUDA – What is is and why it is used?

CUDA, or Compute Unified Device Architecture, is a parallel computing platform and programming model developed by NVIDIA. It enables developers to leverage the immense processing power of NVIDIA GPUs for general-purpose computing tasks. In the context of game and tech development, CUDA is crucial for accelerating complex computational tasks, such as physics simulations, AI algorithms, and real-time image processing, which are computationally intensive and require high throughput. By utilizing CUDA, developers can significantly enhance the performance of their applications, achieving faster processing times and enabling more sophisticated and visually rich gaming experiences. CUDA’s support for C, C++, and Fortran, along with its extensive library ecosystem, makes it a versatile and powerful tool for optimizing and innovating in game and tech development.

CPU processing versus GPU processing differs significantly in their approach to parallelization. A CPU (Central Processing Unit) is designed for general-purpose computing, with a few cores optimized for sequential serial processing. In contrast, a GPU (Graphics Processing Unit) consists of thousands of smaller, efficient cores designed for handling multiple tasks simultaneously, making it ideal for parallel processing.

For example, consider the task of rendering a 3D game scene. On a CPU, rendering involves sequentially processing each vertex, pixel, and texture. Given the limited number of cores, this process can become a bottleneck, especially for high-resolution, complex scenes.

On a GPU, however, the same rendering task is divided across thousands of cores. Each core handles a small portion of the scene simultaneously—vertices, pixels, and textures are processed in parallel. This massive parallelism allows the GPU to render scenes much faster than a CPU could.

To illustrate, imagine simulating particle effects in a game. On a CPU, each particle’s position, velocity, and interaction would be calculated one after another. With a GPU, each particle’s calculations are distributed across multiple cores, allowing thousands of particles to be processed concurrently. This parallel approach dramatically reduces the overall computation time, enabling real-time simulation of complex effects, which is essential for modern gaming and interactive applications.