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The main motivation is to avoid the expensive factorizations that are often needed by Krylov solvers when the problem is generalized or interior eigenvalues are desired. In Davidson\u2010type methods, the factorization is replaced by iterative linear solvers that can be accelerated by a smart preconditioner. Jacobi\u2013Davidson is one of the most effective variants. However, parallel implementations of this method are not widely available, particularly for non\u2010symmetric problems. We present a parallel implementation that has been included in SLEPc, the Scalable Library for Eigenvalue Problem Computations, and test it in the context of a highly scalable plasma turbulence simulation code. We analyze its parallel efficiency and compare it with a Krylov\u2013Schur eigensolver. 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