Detailed analysis of cybersecurity intelligence in various data is essential to counter the recent advanced and complex evolution of cyber security attacks and threats. In particular, highly sophisticated learning models are required to classify cyberattacks and threats or extract security intelligence from unstructured data described in natural language.

The Kalray MPPA-256 processor is based on a recent low-energy manycore architecture. In this article, we investigate its performance in multiprecision arithmetic for number-theoretic applications. We have developed a library for modular arithmetic that takes full advantage of the particularities of this architecture. This is in turn used in an implementation of the ECM, an algorithm for integer factorization using elliptic curves. For parameters corresponding to a cryptanalytic context, our implementation compares well to state-of-the-art implementations on GPU, while using much less energy.