1. Architecture: CUDA is a parallel computing platform and programming model developed by NVIDIA, designed specifically for NVIDIA GPUs. On the other hand, ScalaNLP is a machine learning and natural language processing library for Scala programming language that aims to be platform independent.

2. Usage: CUDA is used for programming GPUs primarily for general-purpose processing, enabling high-performance parallel computing. ScalaNLP, on the other hand, is used for implementing machine learning algorithms and natural language processing tasks in Scala, providing a higher-level abstraction for these tasks.

3. Performance Optimization: CUDA allows for fine-grained control over GPU resources, enabling developers to optimize performance for specific applications. ScalaNLP does not have this level of low-level control, focusing more on ease of use and integration with Scala's ecosystem.

4. Community Support: CUDA has a large and active community of developers and researchers working on various applications and research in the field of parallel computing. ScalaNLP, while not as widely used as CUDA, has a growing community of Scala enthusiasts and researchers focused on machine learning and NLP.

5. Programming Paradigm: CUDA follows a data-parallel programming model, where a large dataset is divided into smaller chunks and processed in parallel by multiple threads. ScalaNLP supports various machine learning and NLP algorithms using functional programming paradigms commonly found in Scala.

6. Granularity of Parallelism: CUDA allows developers to define multi-dimensional grids of threads to perform computations, providing a high level of parallelism. ScalaNLP, being a software library, does not have the same level of control over parallelism at the hardware level.

In Summary, CUDA is primarily focused on parallel computing on NVIDIA GPUs with fine-grained control and optimization, while ScalaNLP is a high-level library for machine learning and NLP in Scala with a growing community and focus on ease of use.