Section: Scientific Foundations

Static parallelization and optimization

Participants : Vincent Loechner, Philippe Clauss, Éric Violard, Alexandra Jimborean.

Static optimizations, from source code at compile time, benefit from two decades of research in automatic parallelization: many works address the parallelization of loop nests accessing multi-dimensional arrays, and these works are now mature enough to generate efficient parallel code [27] . Low-level optimizations, in the assembly code generated by the compiler, have also been extensively dealt for single-core and require few adaptations to support multicore architectures. Concerning multicore specific parallelization, we propose to explore two research directions to take full advantage of these architectures. They are described below.

State of the art

Upstream, an easy interprocedural dependence analysis allows to handle complete programs (but recursivity: recursive functions must be transformed into iterative functions). Concerning iterative control we will use the polyhedral model, a formalism developped these last two decades, which allows to represent the execution of a loop nest by scanning a polytope.

When compiling an application, if it contains loop nests with affine bounds accessing scalars or arrays accessed using affine functions, the polyhedral model allows to:

• compute the dependence graph, which describes the order in which the dependent instructions must be executed [35] ;

• generate a schedule, which extracts some parallelism from the dependence graph [36] , [37] ;

• generate an allocation, which assigns a processor (or a core) to a set of iterations of the loop nest to be scanned.

This last allocation step needs a thorough knowledge of the target architecture, as many crucial choices will result in performance hazards: for example, the volume and flow of inter-processor communications and synchronization; the data locality and the effects of the TLB (Translation Lookaside Buffer) and the various cache levels and distributions; or the register allocation optimizations. There are many techniques to control these parameters, and each architecture needs specific choices, of a valid schedule, of a parallel loop iterations distribution (bloc-, cyclic-, or tiled), of a loop-unrolling factor, as well as a memory data layout and a prefetch strategy (when available). They require powerfull mathematical tools, such as counting the number of integer points contained in a parametric polytope.

Our own contributions in this area are significant. Concerning schedule and data placement, we proposed new advances in minimizing the number of communications for parallel architectures [57] and in cache access optimizations [56] [8] . We also proposed essential advances in parametric polytope manipulation [9] , [5] , developped the first algorithm to count integer points in a parametric polytope as an Ehrhart polynomial [3] , and proposed successive improvements of this algorithm [10] [68] . We implemented these results in the free software PolyLib, utilized by many researchers around the world.

Adapting parallelization to multicore architecture

The first research direction to be explored is multicore specific efficient optimizations. Indeed, multicore architectures need specific optimizations, or we will get underlinear accelerations, or even decelerations. Multicore architectures may have the following properties: specific memory hierarchy, with distributed low-level cache and (possibly semi-) shared high level caches; software-controlled memory hierarchies (memory hints, local stores or scratchpads for example); optimized access to contiguous memory addresses or to separate memory banks; SIMD or vectorial execution in groups of cores, and synchronous execution; higher register allocation pressure when several threads use the same hardware (as in GPGPUs for example); etc.

A schedule and an allocation must be chosen wisely in order to obtain good performances. On NVIDIA GPGPUs, using the CUDA language, Baskaran et al. [26] obtained interesting results that have been implemented in their PLuTo compiler framework. However, they are based on many empirical and imprecise techniques, and require simulations to fine-tune the optimizations: they can be improved. Memory hierarchy efficient control is a cornerstone of tomorrow's multicore architectures performance. Compiler-optimizers have to evolve to meet this requirement.

Simulation and (partial-) profiling may however remain necessary in some cases, when static analysis reaches its intrinsic limits: when the execution of a program depends on dynamic parameters, when it uses complex pointer arithmetic, or when it performs indirect array accesses for example (as is often the case in while loops, out of the scope of the classical polyhedral model). In these cases, the compiler should rely on the profiler, and generate a code that interacts with the dynamic optimizer. This is the link with issues 2 and 3 of this research project.

Expressing many potential parallelisms

The dynamic optimizer (issue 3) must be able to exploit various parallel codes to compare them and the best one to choose, possibly swapping from a code to another during execution. The compiler must therefore generate different potentially efficient versions of a code, depending on fixed parameters such as the schedule or the data layout, and dynamic parameters such as the tile size or the unrolling factor.

The compiler then generates many variants of effective parallelism, formally proved by the static analyzer. It may also generate variants of code that have not been formally validated, due to the analyzer limits, and that have to be checked during execution by the dynamic optimizer: hypothetical parallelism. Hypothetical parallelism could be expressed as a piece of code, valid under certain conditions. Effective and hypothetical parallelisms are called potential parallelism. The variants of potential parallelism will be expressed in an intermediate language that has to be discovered.

Using compiler directives is an interesting way to define this intermediate language. Among the usual directives, we distinguish schedule directives for shared memory architectures (such as the OpenMP(http://www.openmp.org ) parallel directive), and placement directives for distributed memory architectures (for example the HPF(http://hpff.rice.edu ) ALIGN directive). These two types of directives are conjointly necessary to take full profit of multicore architectures. However, we have to study their complementarity and solve the interdependence or conflict that may arise between them. Moreover, new directives should allow to control data transfers between different levels of the memory hierarchy.

We are convinced that the definition of such a language is required in the next advances in compilation for multicore architectures, and there does not exist such an ambitious project to our knowledge. The OpenCL project(http://www.khronos.org/opencl ), presented as an general-purpose and efficient multicore programming environment, is too low-level to be exploitable. We propose to define a new high level language based on compilation directives, that could be used by the skilled programmer or automatically generated by a compiler-optimizer (like OpenMP, recently integrated in the gcc compiler suite).