Mircea Namolaru

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Tuning compiler optimizations for rapidly evolving hardware makes porting and extending an optimizing compiler for each new platform extremely challenging. Iterative optimization is a popular approach to adapting programs to a new architecture automatically using feedback-directed compilation. However, the large number of evaluations required for each(More)
Tuning hardwired compiler optimizations for rapidly evolving hardware makes porting an optimizing compiler for each new platform extremely challenging. Our radical approach is to develop a modular, extensible, self-optimizing compiler that automatically learns the best optimization heuristics based on the behavior of the platform. In this paper we describe(More)
Iterative search combined with machine learning is a promising approach to design optimizing compilers harnessing the complexity of modern computing systems. While traversing a program optimization space, we collect characteristic feature vectors of the program, and use them to discover correlations across programs, target architectures, data sets, and(More)
The iVMX architecture contains a novel vector register file of up to 4096 vector registers accessed indirectly via a mapping mechanism, providing compatibility with the VMX architecture, and potential for dramatic performance benefits [7]. The large number of vector registers and the unique indirection mechanism pose compilation challenges to be used(More)
Heuristics in compilers are often designed by manually analyzing sample programs. Recent advances have successfully applied machine learning to automatically generate heuristics. The typical format of these approaches reduces the input loops, functions or programs to a finite vector of features. A machine learning algorithm then learns a mapping from these(More)
D. Edelsohn W. Gellerich M. Hagog D. Naishlos M. Namolaru E. Pasch H. Penner U. Weigand A. Zaks The GCC (GNU Compiler Collection) project of the Free Software Foundation has resulted in one of the most widespread compilers in use today that is capable of generating code for a variety of platforms. Since 1987, many volunteers from academia and the private(More)
Tuning hardwired compiler optimizations for rapidly evolving hardware makes porting an optimizing compiler for each new platform extremely challenging. Our radical approach is to develop a modular, extensible, self-optimizing compiler that automatically learns the best optimization heuristics based on the behavior of the platform. In this paper we describe(More)
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