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Section: Scientific Foundations

Profiling and execution behavior modeling

Participants : Alain Ketterlin, Philippe Clauss, Benoît Pradelle.

The increasing complexity of programs and hardware architectures makes it ever harder to characterize beforehand a given program's run time behavior. The sophistication of current compilers and the variety of transformations they are able to apply cannot hide their intrinsic limitations. As new abstractions like transactional memories appear, the dynamic behavior of a program strongly conditions its observed performance. All these reasons explain why empirical studies of sequential and parallel program executions have been considered increasingly relevant. Such studies aim at characterizing various facets of one or several program runs, e.g., memory behavior, execution phases, etc. In some cases, such studies characterize more the compiler than the program itself. These works are of tremendous importance to highlight all aspects that escape static analysis, even though their results may have a narrow scope, due to the possible incompleteness of their input data sets.

Selective profiling and interaction with the compiler

In its simplest form, studying a given program's run time behavior consists in collecting and aggregating statistics, e.g., counting how many times routines or basic blocks are executed, or counting the number of cache misses during a certain portion of the execution. In some cases, data can be collected about more abstract events, like the garbage-collector frequency or the number and sizes of sent and received messages. Such measures are relatively easy to obtain, are frequently used to quantify the benefits of some optimization, and may suggest some way to improve performance. These techniques are now well-known, but mostly for sequential programs.

These global studies have often been complemented by local, targeted techniques focused on some program portions, e.g., where static techniques remain inconclusive for some fixed duration. These usages of profiling are usually strongly related to the optimization they complement, and are set up either by the compiler or by the execution environment. Their results may be used immediately at run time, in which case they are considered a form of run time optimization [1] . They can also be used offline to provide hints to a subsequent compilation cycle, in which case they constitute a form of profile-guided compilation, a strategy that is common in general purpose compilers.

For instance, in the context where a set of possible parallelizations have been provided by the compiler (see issue 1), a profiling component can easily be made responsible for testing some relevant condition at run time (e.g., that depends on input data) and for selecting the best between various versions of the code. Beyond such simple tasks, we expect that profiling will, at the beginning of the execution, have enough resources to conduct more elaborate analyzes. We believe that combining an “open” static analysis with an integrated profiling component is a promising approach, first because it may relieve the programmer of a large part of the tedious task of implementing the distribution of computations, and second to free the compiler of the obligation to choose between several optimizations in the absence of enough relevant data. The main open question here is to define precisely the respective roles of the compiler and the profiler, and also the amount and nature of information the former can transmit to the latter.

Profiling and dynamic optimization

In the context of dynamic optimization, that is, when the compiler's abilities have been exhausted, a profiler can still do useful work, provided some additional capabilities [1] . If it is able to instrument the code the way, e.g., a PIN-tool does  [52] , it has access to the whole program, including libraries (or, for example, the code of a low-level library called from a scripting language). This means that it has access to portions of the program that were not under the compiler's control. The profiler can then perform dynamic inter-procedural analyzes, for instance to compute dependencies to detect parallelism that wasn't apparent at compile time because of a function call in the body of a loop. More generally, if the profiler is able to reconstruct at run time some representation of the whole program, as in  [71] for example, it is possible to let it search for any construct that can be optimized and/or parallelized in the context of the current execution. Several virtual machines, e.g., for Java or Microsoft CLR, have opened this way of optimizing programs, probably because virtual machines need to maintain an intermediate, structured representation of the running program.

The possibility of running programs on architectures that include a large number of computing cores has given rise to new abstractions  [69] , [43] , [27] . Transactional memories, for instance, aim at simplifying the management of conflicting concurrent accesses to a shared memory, a notoriously difficult problem  [45] . However, the performance of a transaction-based application heavily depends on its dynamic behavior, and too many conflicting accesses and rollbacks, severely affect performance. We bet that the need for multicore specific programming tools will lead to other abstractions based on speculative execution. Because of the very nature of speculation, all these abstractions will require run time evaluation, and maybe correction, to avoid pathological cases. The profiler has a central role here, because it can be made responsible for diagnosing inefficient use of speculative execution, and for taking corrective action, which means that it has to be integrated to the execution environment. We also think that the large scope and almost infinite potential uses of a profiling component may well suggest new parallel program abstractions, specially targeted at run time evaluation and adaptation.

Run time program modeling

When profiling goes beyond simple aggregation of counts, it can, for example, sample a program's behavior and split its execution into phases. These phases may help target a subsequent evaluation on a new architecture  [63] . When profiling instruments the whole program to obtain a trace, e.g., of memory accesses, it is possible to use this trace for:

Handling such large execution traces, and especially compressing them, is a research topic by itself  [28] , [54] . Our contribution to this topic [7] is unusual in that the result of compression is a sequence of loop nests where memory accesses and loop bounds are affine functions of the enclosing loop indices. Modeling a trace this way leads to slightly better average compression rates compared to other, less expressive techniques. But more importantly, it has the advantage to provide a result in symbolic form, and this result can be further analyzed with techniques usually restricted to the static analysis of source code. We plan to apply, in the short term, similar techniques to the modeling of dynamic dependencies, so as to be able to automatically extract parallelism from program traces.

This kind of analysis is representative of a new kind of tools than could be named “parallelization assistants”  [49] , [59] . Properties that can't be detected by the compiler but that appear to hold in one or several executions of a program can be submitted to the programmer, maybe along a suitable reformulation of its program using some class of abstraction, e.g., compiler directives. The goal is to provide help and guidance in adapting source code, in the same way a classical profiling tool helps pinpoint performance bottlenecks. Control and data dependencies are fundamental to such a tool. An execution trace provides an observed reality; for example a trace of memory addresses. If the observed dynamic dependencies provide a set of constraints, they also suggest a complete family of potential correct executions, be they parallel or sequential, and all these executions are equivalent to the reference execution. Being able to handle large traces, and representing them in some manageable way, means being able to highlight medium to large grain parallelism, which is especially interesting on multicore architectures and often difficult for compilers to discover, for example because of the use of pointers and the difficulty of eliminating potential aliasing. This can be seen as a machine learning problem, where the goal is to recover a hidden structure from a large sequence of events. This general problem has various incarnations, depending on how much the learner knows about the original program, on the kind of data obtained by profiling, on the class of structures sought, and on the objectives of the analysis. We are convinced that such studies will enrich our understanding of the behavior of programs, and of the programming concepts that are really useful. It will also lead to useful tools, and will open up new directions for dynamic optimization.


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