Kompute is the foundational computational engine designed to power the Kotlin-For-Science ecosystem. At its heart, Kompute represents a fundamental shift in how scientific computing can be approached in the Kotlin ecosystem, combining the elegance and safety of Kotlin with the performance demands of scientific computation.
The driving philosophy behind Kompute is that scientific computing shouldn't require compromises between code clarity, type safety, and performance. Traditional scientific computing often forces developers to choose between writing clear, maintainable code and achieving optimal performance. Kompute aims to bridge this gap by providing a layer of abstraction that maintains Kotlin's strengths while delivering the performance expected in scientific computing.
Kompute aims to provide a set of toolkit ranging from type definitions, optimization methods, integrations, and bindings. Which in our opinion would result in writing clean and efficient code for scientific research.
The core of Kompute is built around a powerful expression system. Unlike traditional numerical libraries that work directly with numbers and arrays, Kompute's expression system allows for symbolic representation of mathematical concepts. This approach enables several key capabilities:
- Symbolic manipulation of mathematical expressions (through available symbolic computations kernels such as Mathematica)
- Expression optimization before computation (Optimizing code to the scale of rewriting)
- Seamless integration with higher-level mathematical concepts
For example, when you write an expression in Kompute, it exists first as a symbolic representation that can be analyzed, optimized, and transformed before any actual computation occurs. This allows Kompute to make intelligent decisions about how to compute the result most efficiently.
The Expression system will be also a gateway into implementing an ecosystem of libraries that are built on top of Kompute and each other, making them both integrable with each other and efficient by nature.
One of Kompute's most innovative features is its ability to make intelligent decisions about how computations should be performed. Rather than always using the same approach, Kompute analyzes each computation request and chooses the most efficient method based on multiple factors:
- The size and structure of the data
- The complexity of the operations
- Available computational resources
- Historical performance data
- Current system load
For instance, when performing matrix multiplication, Kompute might choose between:
- Using native Kotlin code for small matrices
- Calling optimized BLAS libraries for larger matrices
- Utilizing GPU computation for massive matrices
- Distributing the computation across multiple threads
This decision-making process is transparent to the user (although it is adjustable with users command) while ensuring optimal performance.
Scientific computing often involves large datasets and complex computations that can strain memory resources. Kompute takes a proactive approach to memory management through:
- Smart buffer allocation and reuse
- Memory pooling for frequent operations
- Cache-aware computation strategies
- Intelligent garbage collection coordination
- Automatic memory layout optimization
These optimizations happen automatically (and manually), allowing developers to focus on their algorithms rather than memory management details.
Scientific computing has a rich history, with valuable libraries written in C, C++, and Fortran. Rather than reinventing these tested solutions, Kompute provides a sophisticated binding system that:
- Optimizes calls to native code to minimize overhead
- Manages memory efficiently across language boundaries
- Provides type-safe interfaces to untyped libraries
- Batches operations to minimize cross-language calls
This means you can leverage existing high-performance libraries while writing clean, modern Kotlin code.
Modern scientific computing often requires parallel processing capabilities. Kompute provides a unified approach to parallelism that includes:
- Automatic thread management for CPU-based parallelism
- GPU computation for suitable workloads
- Distributed computing capabilities
- Load balancing and work distribution
- Resource monitoring and optimization
The key innovation here is that Kompute handles the complexity of parallel computation while presenting a clean, sequential-looking API to the developer.
Unlike many scientific computing libraries that sacrifice type safety for performance, Kompute maintains strong type safety while achieving high performance through:
- A sophisticated type system that captures mathematical properties
- Compile-time validation of operations
- Automatic type conversion when safe
- Clear error messages when operations are invalid
- Zero-cost abstractions where possible
At the core of Kompute's performance is its binding decision engine. This system:
- Analyzes each operation or series of operations
- Determines the optimal execution strategy
- Manages the execution across different computation backends
- Learns from previous execution results
- Adapts to changing conditions
This engine is what allows Kompute to provide optimal performance without requiring developer intervention.
The practical impact of Kompute's design is significant:
- Developers can write clear, maintainable code that performs at native speeds
- Existing C/C++/Fortran libraries can be seamlessly integrated
- Complex mathematical operations can be expressed clearly and executed efficiently
- Memory usage is optimized automatically
- Parallel computing capabilities are accessible without parallel programming complexity
Kompute is designed to grow with the scientific computing ecosystem. Key areas of future development include:
- Enhanced GPU computing capabilities
- Expanded symbolic computation features
- Additional library bindings
- Improved distributed computing capabilities
- Advanced optimization techniques
In later section we would investigate each of these point separately.