Abstract. Matlab has gained widespread acceptance among scientists. Several dynamic aspects of the language contribute to its appeal, but also provide many challenges. One such problem is caused by the copy semantics of Matlab. Existing Matlab systems rely on reference-counting schemes to create copies only when a shared array representation is updated. This reduces array copies, but requires runtime checks.We present a staged static analysis approach to determine when copies are not required. The first stage uses two simple, intraprocedural analyses, while the second stage combines a forward necessary copy analysis with a backward copy placement analysis. Our approach eliminates unneeded array copies without requiring reference counting or frequent runtime checks.We have implemented our approach in the McVM JIT. Our results demonstrate that, for our benchmark set, there are significant overheads for both existing reference-counted and naive copy-insertion approaches, and that our staged approach is effective in avoiding unnecessary copies.
On-stack replacement (OSR) is a technique that allows a virtual machine to interrupt running code during the execution of a function/method, to re-optimize the function on-the-fly using an optimizing JIT compiler, and then to resume the interrupted function at the point and state at which it was interrupted. OSR is particularly useful for programs with potentially long-running loops, as it allows dynamic optimization of those loops as soon as they become hot. This paper presents a modular approach to implementing OSR for the LLVM compiler infrastructure. This is an important step forward because LLVM is gaining popular support, and adding the OSR capability allows compiler developers to develop new dynamic techniques. In particular, it will enable more sophisticated LLVM-based JIT compiler approaches. Indeed, other compiler/VM developers can use our approach because it is a clean modular addition to the standard LLVM distribution. Further, our approach is defined completely at the LLVM-IR level and thus does not require any modifications to the target code generation. The OSR implementation can be used by different compilers to support a variety of dynamic optimizations. As a demonstration of our OSR approach, we have used it to support dynamic inlining in McVM. McVM is a virtual machine for MATLAB which uses a LLVM-based JIT compiler. MATLAB is a popular dynamic language for scientific and engineering applications that typically manipulate large matrices and often contain long-running loops, and is thus an ideal target for dynamic JIT compilation and OSRs. Using our McVM example, we demonstrate reasonable overheads for our benchmark set, and performance improvements when using it to perform dynamic inlining.
MATLAB is a popular dynamic array-based language used by engineers, scientists and students worldwide. The built-in function feval is an important MATLAB feature for certain classes of numerical programs and solvers which benefit from having functions as parameters. Programmers may pass a function name or function handle to the solver and then the solver uses feval to indirectly call the function. In this paper, we show that there are significant performance overheads for function calls via feval, in both MATLAB interpreters and JITs. The paper then proposes, implements and compares two on-the-fly mechanisms for specialization of feval calls. The first approach uses on-stack replacement technology, as supported by McVM/McOSR. The second approach specializes calls of functions with feval using a combination of runtime input argument types and values. Experimental results on seven numerical solvers show that the techniques provide good performance improvements.
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