As usual, the full code for this article can be found over on GitHub. Each is optimized to create the matching kind of matrix. Let's create the one expected as the result of their multiplication: Now that everything is set up, let's implement the multiplication algorithm. Among other things, ND4J offers matrix computation features. We then have to create a state object containing our arrays: That way, we make sure arrays initialization is not part of the benchmarking. When we run this benchmarking, we obtain completely different results: As we can see, the homemade implementations and the Apache library are now way worse than before, taking nearly 10 minutes to perform the multiplication of the two matrices. In multiplication columns in matrix1 must be equal to rows in matrix2. But, it offers an alternative: the isIdentical() method which takes not only another matrix parameter but also a double fault tolerance one to ignore small differences due to double precision: That concludes matrices multiplication with the EJML library. EJML and LA4J are performing pretty well as they run in nearly 30 seconds. //
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