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We introduce giotto-tda, a Python library that integrates high-performance topological data analysis with machine learning via a scikit-learn-compatible API and state-of-the-art C++ implementations. The library's ability to handle various types of data is rooted in a wide range of preprocessing techniques, and its strong focus on data exploration and interpretability is aided by an intuitive plotting API. Source code, binaries, examples, and documentation can be found at https://github.com/giotto-ai/giotto-tda.

In recent years two main platforms emerged as powerful key players in the domain of parallel computing: GPUs and FPGAs. Many researches investigate interaction and benefits of coupling them with a general purpose processor (CPU), but very few, and only very recently, integrate the two in the same computational system. Even less research are focusing on direct interaction of the two platforms [1]. This paper presents an open source framework enabling easy integration of GPU and FPGA resources; Our work provides direct data transfer between the two platforms with minimal CPU coordination at high data rate and low latency. Finally, at the best of our knowledge, this is the first proposition of an open source implementation of a system including an FPGA and a GPU that provides code for both sides.Notwithstanding the generality of the presented framework, we present in this paper an actual implementation consisting of a single GPU board and a FPGA board connected through a PCIe link. Measures on this implementation demonstrate achieved data rate that are close to the theoretical maximum.

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