BIDMat is a matrix library intended to support large-scale exploratory data analysis. BIDMat is designed to be both fast and usable. Its speed comes from a combination of hardware-accelerated (CPU and GPU) kernels and roofline design: kernels have been systematically optimized against theoretical limits. BIDMat is written in the Scala and inherits many of its virtues. Our tests show that it is much faster than any other similar tool (see the benchmarks in its sister project, BIDMach). The goals of BIDMat are:

1. To provide an interactive data analysis environment, similar to R or scipy or Matlab. Thanks to the Scala language, we inherit a REPL, a very flexible operator syntax (including unicode math operators), and a very clean concurrent programming model using actors.

2. To leverage native machine performance through native libraries (Intel MKL, HDF5, CUDA and string/XML processing). Java/Scala are excellent high-level languages, but are one or two orders of magnitude away from native performance in some key areas: especially matrix algebra and string processing, and below the bar to a lesser degree in File-IO.

3. To leverage GPU hardware and GPU-based data as a first-class object. GPUs now offer large improvements (again one or more orders of magnitude) over CPU performance in many areas that are relevant to data mining: matrix algebra, transcendental functions, random number generation. These advantages in low-level operations carry over to network and graph algorithms and even natural language parsing.

Documentation is here on the github wiki page.