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 program has prevented iterative compilation from widespread take-up in production compilers. Machine learning has been proposed to tune optimizations across programs systematically but is currently limited to a few transformations, long training phases and critically lacks publicly released, stable tools.Our approach is to develop a modular, extensible, self-tuning optimization infrastructure to automatically learn the best optimizations across multiple programs and architectures based on the correlation between program features, run-time behavior and optimizations. In this paper we describe Milepost GCC, the first publicly-available open-source machine learning-based compiler. It consists of an Interactive Compilation Interface (ICI) and plugins to extract program features and exchange optimization data with the cTuning.org open public repository. It automatically adapts the internal optimization heuristic at function-level granularity to improve execution time, code size and compilation time of a new program on a given architecture. Part of the Milepost technology together with low-level ICI-inspired plugin framework is now included in the mainline GCC.We developed machine learning plugins based on probabilistic and transductive approaches to predict good combinations of optimizations. Our preliminary experimental results show that it is possible to automatically reduce the execution time of individual MiBench programs, some by more than a factor of 2, while also improving compilation 1 INRIA Saclay, France (HiPEAC member) · 2 University of Versailles Saint Quentin en Yvelines, France · 3 IBM Haifa, Israel (HiPEAC member) · 4 CAPS Entreprise, France (HiPEAC member) · 5 ARC International, UK · 6 University of Edinburgh, UK (HiPEAC member) · 2 time and code size. On average we are able to reduce the execution time of the MiBench benchmark suite by 11% for the ARC reconfigurable processor. We also present a realistic multi-objective optimization scenario for Berkeley DB library using Milepost GCC and improve execution time by approximately 17%, while reducing compilation time and code size by 12% and 7% respectively on Intel Xeon processor.

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With the advent of VLSI, relatively large processing arrays may be realized in a single VLSI chip. Such regularly structured arrays take considerably less time to design and test, and fault-tolerance can easily be introduced into them. However, only a few computational algorithms which can effectively use such regular arrays have been developed so far.We present an approach to mapping arbitrary algorithms, expressed as programs in a data flow language, onto a regular array of data-driven processors implemented by a number of VLSI chips. Each chip contains a number of processors, interconnected by a set of regular paths, and connected to processors in other similar chips to form a large array. This array is thus tailored to perform a specific computational task, as an attached processor in a larger system.The data flow program is first translated into a graph representation, the dataflow graph, which is then mapped onto a finite but (theoretically) unbounded array of identical processors. Each node in the graph represents an operation which can be performed by an individual processor in the array. Therefore, the mapping operation consists of assigning nodes in the graph to processors in the array, and defining the connections between the processors according to the arcs in the graph. The last step consists of partitioning the unbounded array into a number of segments, to account for the number of processors which fit in a single VLSI chip.

In this paper we describe how a profiling system can be successfully used to restructure the components of an operating system for improved overall performance. We discuss our choice of a profiling system and how it was agplied to the AS1400 (Application System1400) operating system for the purpose of reordering code. Previous work in the industry has been mainly useful only for application programs. Our work demonstrates how such techniques can be applied to operating system code, while preserving maintainability of the operating system in the customer's environment. Wopyright 1998 by International Business Machines Corporation. Copying in printed form for private use is permitted without payment of royalty provided that (1) each reproduction is done without alteration and (2) the Journal reference and IBM copyright notice are included on the first page. The title and abstract, but no other portions, of this paper may be copied or distributed royalty free without further permission by computer-based and other information-service systems. Permission to republish any other portion of this paper must be obtained from the Editor.

Instrumentation is commonly used to track application behavior: to collect program profiles; to monitor component health and performance; to aid in component testing; and more. Program annotation enables developers and tools to pass extra information to later stages of software development and execution. For example, the .NET runtime relies on annotations for a significant chunk of the services it provides. Both mechanisms are evolving into important parts of software development in the context of modern platforms such as Java and .NET.Instrumentation tools are generally not aware of the semantics of information passed via the annotation mechanism. This is especially true for post-compiler, e.g., runtime, instrumentation. The problem is that instrumentation may affect the correctness of annotations, rendering them invalid or misleading, and producing unforeseen side-effects during program execution. This problem has not been addressed so far.In this paper, we show the subtle interaction that takes place between annotations and instrumentation using several real-life examples. Many annotations are intended to provide information for the runtime; the virtual environment is a prominent annotation consumer, and must be aware of this conflict. It may also be required to provide runtime support to other annotation consumers. We propose an annotation taxonomy and show how instrumentation affects various annotations that were used in research and in industrial applications. We show how the annotations can expose enough information about themselves to prevent the instrumentation from accidentally corrupting the annotations. We demonstrate this approach on our annotations benchmark.