One glaring weakness of Java for numerical programming is its lack of support for complex numbers. Simply creating a Complex number class leads to poor performance relative to Fortran. We show in this paper, however, that the combination of such a Complex class and a compiler that understands its semantics does indeed lead to Fortran-like performance. This performance gain is achieved while leaving the Java language completely unchanged and maintaining full compatibility with existing Java Virtual Machines. We quantify the effectiveness of our approach through experiments with linear algebra, electromagnetics, and computational fluid-dynamics kernels. 1 Introduction The Java Grande Forum has identified several critical issues related to the role of Java (TM)1 in numerical computing [14]. One of the key requirements is that Java must support efficient operations on complex numbers. Complex arithmetic and access to elements of complex arrays must be as efficient as the manipulation o...

We present a new limited form of interprocedural analysis called field analysis that can be used by a compiler to reduce the costs of modern language features such as objectoriented programming, automatic memory management, and run-time checks required for type safety. Unlike many previous interprocedural analyses, our analysis is cheap, and does not require access to the entire program. Field analysis exploits the declared access restrictions placed on fields in a modular language (e.g. field access modifiers in Java) in order to determine useful properties of fields of an object. We describe our implementation of field analysis in the Swift optimizing compiler for Java, as well a set of optimizations that exploit the results of field analysis. These optimizations include removal of run-time tests, compiletime resolution of method calls, object inlining, removal of unnecessary synchronization, and stack allocation. Our results demonstrate that field analysis is efficient and effectiv...

Object-oriented languages such as Java and Smalltalk provide a uniform object reference model, allowing objects to be conveniently shared. If implemented directly, these uniform reference models can suffer in efficiency due to additional memory dereferences and memory management operations. Automatic inline allocation of child objects within parent objects can reduce overheads of heap-allocated pointer-referenced objects. We present three compiler analyses to identify inlinable fields by tracking accesses to heap objects. These analyses span a range from local data flow to adaptive whole-program, flow-sensitive inter-procedural analysis. We measure their cost and effectiveness on a suite of moderate-sized C++ programs (up to 30,000 lines including libraries). We show that aggressive interprocedural analysis is required to enable object inlining, and our adaptive inter-procedural analysis [23] computes precise information efficiently. Object inlining eliminates typically 40% of object a...