Parallel Processing in GPUs? Introducing: Bend
Bend: Unlocking GPU Programming?, Thomas Reid, Medium
Once solely focused on rendering dazzling visuals, GPUs have become versatile workhorses for various computational tasks. The secret to this lies in parallel processing. Thousands of cores inside the GPU can execute several operations in parallel, making them ideal for applications whose tasks can be broken down into smaller ones that are independent of each other. This has allowed platforms such as machine learning, scientific computing, and data analysis to make huge leaps in the last few years.
The Roadblock: Traditional GPU Programming
There is huge computing power in GPUs, but leveraging that computing power is by no means an easy task. Conventional languages, including CUDA and OpenCL, require the developer to manage low-level memory allocation, thread synchronization, among other factors. This task is cumbersome, prone to errors, and a barrier to non-experts.
Introducing Bend: The Simplified Way
Along this line, the authors have proposed a new language, Bend, which fills this gap. Bend is a high-level, declarative interface for programming modern GPUs. It abstracts all of the heavy details from the underlying hardware and frees the developer to focus on the core logic of the algorithm in hand.
What Makes Bend Special?
Declarative Syntax: Bend encourages concise and readable code syntax. It has a declarative syntax since it states what the developers want to achieve without describing how that would be achieved.
Automatic Parallelization: The automatic selection of Bend over which tasks can execute in parallel on multiple GPU cores, saves the developer from thread management and synchronization itself.
Memory Management: No more painful memory management; Bend does this regard by allocating and deallocating the memory on the GPU.
Integration with Other Tools: Bend also plays nice with a variety of popular programming languages and development environments, extending reach to a wider audience.
Benefits of Embracing Bend
Increased Productivity: By simplifying GPU programming, Bend hugely increases productivity for developers.
Performance Benefits: Bend’s efficient parallelization translates to real performance increases on GPU hardware.
Accessibility for All: The high-level interface makes GPU programming more accessible, even for those without deep expertise.
The Future of GPU Computing
Bend is a critical step towards democratizing programming on the GPU. Due to its abstraction and declarative nature, it allows a developer to leverage the capabilities of GPUs across applications it has not done traditionally. Since Bend is evolving continuously, it has the potential to redefine the state-of-the-art best practices regarding harnessing computing power from the GPU.
Sources:
Exploring Bend: A Revolutionary Language for GPU Programming: https://medium.com/tag/bend
Everything That Can Run in Parallel Will Run in Parallel: https://medium.com/@muhammedashraf2661/cuda-programming-an-introduction-to-gpu-architecture-dfd8dfffa13f
Bend: a new GPU-native language: https://discourse.julialang.org/t/state-of-ad-in-2024/112601
