Ruby gem embedded in a glowing circuit board, representing modular GPU code.

Unlock GPU Power: How Modular Programming Simplifies Complex Tasks

Soraya Malik in Tech & Innovation September 2025 • 4 min read.

"Discover how a new Ruby extension, Ikra, is revolutionizing GPU computing by enabling developers to create modular, high-performance code with ease."

Graphics Processing Units (GPUs) have become indispensable tools for tackling computationally intensive tasks across various fields, from scientific simulations to neural networks. The demand for efficient GPU programming has led to two primary approaches: low-level coding using CUDA or OpenCL, and high-level language extensions that offer better productivity.

While many high-level extensions cater to statically-typed languages, a significant number of programmers prefer the simplicity and flexibility of dynamically-typed languages. This is where Ikra comes in—a Ruby extension designed to make array-based GPU computing more accessible and efficient.

Ikra allows programmers to compose GPU programs from multiple reusable parallel sections, which are then fused into a small number of GPU kernels. This approach not only simplifies development but also achieves performance levels comparable to hand-written CUDA code.

Ikra: Modular GPU Computing Redefined

Ruby gem embedded in a glowing circuit board, representing modular GPU code.

Ikra introduces a programming style that encourages modularity and integration of dynamic language features. This means that while parallel sections are limited to a restricted set of types and operations, the rest of the code can freely use all of Ruby's features, including metaprogramming and external libraries.

One of Ikra's key innovations is its ability to fuse multiple kernels into a single, optimized kernel. This reduces data transfer overhead and allows data to remain in registers, significantly improving performance. Additionally, loops surrounding parallel code are compiled to C++, further boosting efficiency.

Here’s how Ikra makes GPU programming more accessible:

Modularity: Compose programs from reusable, smaller kernels.
Dynamic Language Integration: Seamlessly blend Ruby and GPU code.
Kernel Fusion: Optimize performance by merging multiple kernels.
Loop Compilation: Compile loops to C++ for added efficiency.

To better understand how Ikra achieves this, let's delve into its compilation process. When a parallel operation is invoked in the Ruby interpreter, Ikra executes it symbolically. This involves retrieving the source code, generating abstract syntax trees, and inferring types. The result is an array command object, which contains all the information needed for CUDA code generation and execution. This just-in-time (JIT) compilation approach allows Ikra to optimize GPU programs based on runtime type information.

The Future of Ikra

While Ikra represents a significant step forward in making GPU programming more accessible, there's still room for improvement. Future work will focus on extending kernel fusion to stencil operations and improving memory management to reduce allocation overhead. By continuing to bridge the gap between high-level languages and GPU performance, Ikra promises to unlock the full potential of parallel computing for a wider range of developers.

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Everything You Need To Know

What is Ikra, and how does it simplify GPU computing?

Ikra is a Ruby extension designed to simplify array-based GPU computing. It enables developers to construct GPU programs from reusable, parallel sections, which are then combined into a smaller number of GPU kernels. This not only makes development easier but also leads to performance comparable to hand-written CUDA code.

How does Ikra achieve modularity and integrate dynamic language features?

Ikra achieves modularity by allowing programmers to compose GPU programs from multiple reusable parallel sections. It allows for the integration of dynamic Ruby language features. Parallel sections are limited to a restricted set of types and operations, while the rest of the code can freely use all of Ruby's capabilities, including metaprogramming and external libraries. Key innovations include kernel fusion and compilation of loops to C++.

What is kernel fusion in Ikra, and how does it improve performance?

Kernel fusion in Ikra combines multiple kernels into a single, optimized kernel. This reduces the overhead of data transfer and allows data to remain in registers, leading to a significant boost in performance. By minimizing data movement between CPU and GPU memory, Ikra improves overall efficiency.

How does Ikra's just-in-time (JIT) compilation process work?

When a parallel operation is invoked in the Ruby interpreter, Ikra executes it symbolically. This involves retrieving source code, generating abstract syntax trees, and inferring types. This generates an array command object, which contains the information required for CUDA code generation and execution. This just-in-time (JIT) compilation approach allows Ikra to optimize GPU programs based on runtime type information.

What are the future development plans for Ikra, and how will they improve its capabilities?

Future development of Ikra will focus on extending kernel fusion to stencil operations and enhancing memory management to decrease allocation overhead. By further bridging the gap between high-level languages and GPU performance, Ikra aims to unlock the full potential of parallel computing for a broader range of developers. Addressing memory allocation is crucial for handling larger datasets and more complex computations efficiently.