@TechReport{	  it:2019-007,
  author	= {Malin K{\"a}ll{\'e}n and Tobias Wrigstad},
  title		= {Performance of an {OO} Compute Kernel on the {JVM}:
		  Revisiting {J}ava as a Language for Scientific Computing
		  Applications ({E}xtended Version)},
  institution	= {Department of Information Technology, Uppsala University},
  year		= {2019},
  number	= {2019-007},
  month		= sep,
  abstract	= {The study of Java as a programming language for scientific
		  computing is warranted by simpler, more extensible and more
		  easily maintainable code. Previous work on refactoring a
		  C++ scientific computing code base to follow best practises
		  of object-oriented software development revealed a coupling
		  of such practises and considerable slowdowns due to
		  indirections introduced by abstractions. In this paper, we
		  explore how Java's JIT compiler handle such
		  abstraction-induced indirection using a typical scientific
		  computing compute kernel extracted from a linear solver
		  written in C++. We find that the computation times for
		  large workloads on one machine can be on-pair for C++ and
		  Java. However, for distributed computations, a better
		  parallelisation strategy needs to be found for non-blocking
		  communication. We also report on the impact on performance
		  for common ``gripes'': garbage collection, array bounds
		  checking, and dynamic binding.}
}