Number Of Available Registers Research Articles

Efficient Spilling Reduction for Software Pipelined Loops in Presence of Multiple Register Types in Embedded VLIW Processors

Integrating register allocation and software pipelining of loops is an active research area. We focus on techniques that precondition the dependence graph before software pipelining in order to ensure that no register spill instructions are inserted by the register allocator in the software pipelined loop. If spilling is not necessary for the input code, preconditioning techniques insert dependence arcs so that the maximum register pressure MAXLIVE achieved by any loop schedule is below the number of available registers, without hurting the initiation interval if possible. When a solution exists, a spill-free software pipeline is guaranteed to exist. Existing preconditioning techniques consider one register type (register class) at a time [Deschinkel and Touati 2008]. In this article, we extend preconditioning techniques so that multiple register types are considered simultaneously. First, we generalize the existing theory of register pressure minimization for cyclic scheduling. Second, we implement our method inside the production compiler of the ST2xx VLIW family, and we demonstrate its efficiency on industry benchmarks (FFMPEG, MEDIABENCH, SPEC2000, SPEC2006). We demonstrate a high spill reduction rate without a significant initiation interval loss.

Periodic register saturation in innermost loops

This article treats register constraints in high performance codes and embedded VLIW computing, aiming to decouple register constraints from instruction scheduling. It extends the register saturation (RS) concept to periodic instruction schedules, i.e., software pipelining (SWP). We formally study an approach which consists in computing the exact upper-bound of the register need for all the valid SWP schedules of an innermost loop (without overestimation), independently of the functional unit constraints. We call this upper-limit the periodic register saturation (PRS) of the data dependence graph (DDG). PRS is well adapted to situations where spilling is not a favourite solution for reducing register pressure compared to ILP scheduling: spill operations request memory data with a higher energy consumption. Also, spill code introduces unpredictable cache effects and makes Worst-Case-Execution-Time (WCET) estimation less accurate. PRS is a computer science concept that has many possible applications. First, it provides compiler designers new ways to generate better codes regarding register optimisation by avoiding useless spilling. Second, its computation can help architectural designers to decide about the most suitable number of available registers inside an embedded VLIW processor; such architectural decision can be done with full respect to instruction level parallelism extraction, independently of the chosen functional units configuration. Third, it can be used to verify prior to instruction scheduling that a code does not require spilling. In this paper, we write an appropriate mathematical formalism for the problem of PRS computation and reduction in the case of loops where data dependence graphs may be cyclic. We prove that PRS reduction is NP-hard and we provide optimal and approximate methods. We have implemented our methods, and our experimental results demonstrate the practical usefulness and efficiency of the PRS approach.

Register Saturation in Instruction Level Parallelism

The registers constraints are usually taken into account during the scheduling pass of an acyclic data dependence graph (DAG): any schedule of the instructions inside a basic block must bound the register requirement under a certain limit. In this work, we show how to handle the register pressure before the instruction scheduling of a DAG. We mathematically study an approach which consists in managing the exact upper-bound of the register need for all the valid schedules of a considered DAG, independently of the functional unit constraints. We call this computed limit the register saturation (RS) of the DAG. Its aim is to detect possible obsolete register constraints, i.e., when RS does not exceed the number of available registers. If it does, we add some serial edges to the original DAG such that the worst register need does not exceed the number of available registers. We propose an appropriate mathematical formalism for this problem. Our generic processor model takes into account superscalar, VLIW and EPIC/IA64 architectures. Our deeper analysis of the problem and our formal methods enable us to provide nearly optimal heuristics and strategies for register optimization in the face of ILP.

Foetal ECG recovery using dynamic neural networks

Non-invasive electrocardiography has proven to be a very interesting method for obtaining information about the foetus state and thus to assure its well-being during pregnancy. One of the main applications in this field is foetal electrocardiogram (ECG) recovery by means of automatic methods. Evident problems found in the literature are the limited number of available registers, the lack of performance indicators, and the limited use of non-linear adaptive methods. In order to circumvent these problems, we first introduce the generation of synthetic registers and discuss the influence of different kinds of noise to the modelling. Second, a method which is based on numerical (correlation coefficient) and statistical (analysis of variance, ANOVA) measures allows us to select the best recovery model. Finally, finite impulse response (FIR) and gamma neural networks are included in the adaptive noise cancellation (ANC) scheme in order to provide highly non-linear, dynamic capabilities to the recovery model. Neural networks are benchmarked with classical adaptive methods such as the least mean squares (LMS) and the normalized LMS (NLMS) algorithms in simulated and real registers and some conclusions are drawn. For synthetic registers, the most determinant factor in the identification of the models is the foetal–maternal signal-to-noise ratio (SNR). In addition, as the electromyogram contribution becomes more relevant, neural networks clearly outperform the LMS-based algorithm. From the ANOVA test, we found statistical differences between LMS-based models and neural models when complex situations (high foetal–maternal and foetal-noise SNRs) were present. These conclusions were confirmed after doing robustness tests on synthetic registers, visual inspection of the recovered signals and calculation of the recognition rates of foetal R-peaks for real situations. Finally, the best compromise between model complexity and outcomes was provided by the FIR neural network. Both the methodology for selecting a model and the introduction of advanced neural models are the main contributions of this paper.

A Parametrized Loop Fusion Algorithm for Improving Parallelism and Cache Locality

Loop fusion is a reordering transformation that merges multiple loops into a single loop. It can increase data locality and the granularity of parallel loops, thus improving program performance. Previous approaches to this problem have looked at these two benefits in isolation. In this work, we propose a new model which considers data locality, parallelism and register pressure together. We build a weighted directed acyclic graph in which the nodes represent program loops along with their register pressure, and the edges represent the amount of locality and parallelism present. The direction of an edge represents an execution order constraint. We then partition the graph into components such that the sum of the weights on the edges cut is minimized, subject to the constraint that the nodes in the same partition can be safely fused together, and the register pressure of the combined loop does not exceed the number of available registers. Previous work demonstrates that the general problem of finding optimal partitions is NP-hard. In restricted cases, we show that it is possible to arrive at the optimal solution. We give an algorithm for the restricted case and a heuristic for the general case. We demonstrate the effectiveness of fusion and our approach with experimental results.