This work discusses about CodAl language and Codasip framework. It describes LLVM compiling platform, LLVM IR and its possible optimizations. The result of this work is creation and implementation a proposal of global scheduling dependence on profile as extension in LLVM.

<p>In this thesis we address the problem of optimal code generation for irregular architectures such as Digital Signal Processors (DSPs).</p><p>Code generation consists mainly of three interrelated optimization tasks: instruction selection (with resource allocation), instruction scheduling and register allocation. These tasks have been discovered to be NP-hard for most architectures and most situations. A common approach to code generation consists in solving each task separately, i.e. in a decoupled manner, which is easier from a software engineering point of view. Phase-decoupled compilers produce good code quality for regular architectures, but if applied to DSPs the resulting code is of significantly lower performance due to strong interdependences between the different tasks.</p><p>We developed a novel method for fully integrated code generation at the basic block level, based on dynamic programming. It handles the most important tasks of code generation in a single optimization step and produces an optimal code sequence. Our dynamic programming algorithm is applicable to small, yet not trivial problem instances with up to 50 instructions per basic block if data locality is not an issue, and up to 20 instructions if we take data locality with optimal scheduling of data transfers on irregular processor architectures into account. For larger problem instances we have developed heuristic relaxations.</p><p>In order to obtain a retargetable framework we developed a structured architecture specification language, xADML, which is based on XML. We implemented such a framework, called OPTIMIST that is parameterized by an xADML architecture specification.</p><p>The thesis further provides an Integer Linear Programming formulation of fully integrated optimal code generation for VLIW architectures with a homogeneous register file. Where it terminates successfully, the ILP-based optimizer mostly works faster than the dynamic programming approach; on the other hand, it fails for several larger examples where dynamic programming still provides a solution. Hence, the two approaches complement each other. In particular, we show how the dynamic programming approach can be used to precondition the ILP formulation.</p><p>As far as we know from the literature, this is for the first time that the main tasks of code generation are solved optimally in a single and fully integrated optimization step that additionally considers data placement in register sets and optimal scheduling of data transfers between different registers sets.</p>

In this thesis we address the problem of optimal code generation for irregular architectures such as Digital Signal Processors (DSPs). Code generation consists mainly of three interrelated optimization tasks: instruction selection (with resource allocation), instruction scheduling and register allocation. These tasks have been discovered to be NP-hard for most architectures and most situations. A common approach to code generation consists in solving each task separately, i.e. in a decoupled manner, which is easier from a software engineering point of view. Phase-decoupled compilers produce good code quality for regular architectures, but if applied to DSPs the resulting code is of significantly lower performance due to strong interdependences between the different tasks. We developed a novel method for fully integrated code generation at the basic block level, based on dynamic programming. It handles the most important tasks of code generation in a single optimization step and produces an optimal code sequence. Our dynamic programming algorithm is applicable to small, yet not trivial problem instances with up to 50 instructions per basic block if data locality is not an issue, and up to 20 instructions if we take data locality with optimal scheduling of data transfers on irregular processor architectures into account. For larger problem instances we have developed heuristic relaxations. In order to obtain a retargetable framework we developed a structured architecture specification language, xADML, which is based on XML. We implemented such a framework, called OPTIMIST that is parameterized by an xADML architecture specification. The thesis further provides an Integer Linear Programming formulation of fully integrated optimal code generation for VLIW architectures with a homogeneous register file. Where it terminates successfully, the ILP-based optimizer mostly works faster than the dynamic programming approach; on the other hand, it fails for several larger examples where dynamic programming still provides a solution. Hence, the two approaches complement each other. In particular, we show how the dynamic programming approach can be used to precondition the ILP formulation. As far as we know from the literature, this is for the first time that the main tasks of code generation are solved optimally in a single and fully integrated optimization step that additionally considers data placement in register sets and optimal scheduling of data transfers between different registers sets.

Clustered architecture processors are preferred for embedded systems because centralized register file architectures scale poorly in terms of clock rate, chip area, and power consumption. Scheduling for clustered architectures involves spatial concerns (where to schedule) as well as temporal concerns (when to schedule). Various clustered VLIW configurations, connectivity types, and inter-cluster communication models present different performance trade-offs to a scheduler. The scheduler is responsible for resolving the conflicting requirements of exploiting the parallelism offered by the hardware and limiting the communication among clusters to achieve better performance. Earlier proposals for cluster scheduling fall into two main categories, viz., phase-decoupled scheduling and phase-coupled scheduling and they focus on clustered architectures which provide inter-cluster communication by an explicit inter-cluster copy operation. However, modern commercial clustered architectures provide snooping capabilities (apart from the support for inter-cluster communication using an explicit MV operation) by allowing some of the functional units to read operands from the register file of some of the other clusters without any extra delay. The phase-decoupled approach of scheduling suffers from the well known phase-ordering problem which becomes severe for such a machine model (with snooping) because communication and resource constraints are tightly coupled and thus are exposed only during scheduling. Tight integration of communication and resource constraints further requires taking into account the resource and communication requirements of other instructions ready to be scheduled in the current cycle while binding an instruction, in order to carry out effective binding. However, earlier proposals on integrated scheduling consider instructions and clusters for binding using a fixed order and thus they show different widely varying performance characteristics in terms of execution time and code size. Other shortcomings of earlier integrated algorithms (that lead to suboptimal cluster scheduling decisions) are due to non-consideration of future communication (that may arise due to a binding) and functional unit binding. In this thesis, we propose a pragmatic scheme and also a generic graph matching based framework for cluster scheduling based on a generic and realistic clustered machine model. The proposed scheme effectively utilizes the exact knowledge of available communication slots, functional units, and load on different clusters as well as future resource and communication requirements known only at schedule time to attain significant performance improvement without code size penalty over earlier algorithms. The proposed graph matching based framework for cluster scheduling resolves the phase-ordering and fixed-ordering problem associated with scheduling on clustered VLIW architectures. The framework provides a mechanism to exploit the slack of instructions by dynamically varying the freedom available in scheduling an instruction and hence the cost of scheduling an instruction using different alternatives to reduce the inter-cluster communication. An experimental evaluation of the proposed framework and some of the earlier proposals is presented in the context of a state-of-art commercial clustered architecture.

Media processing applications typically involve numerical blocks that exhibit regular floating-point computation patterns. For processors whose architecture supports only integer arithmetic, these patterns can be profitably turned into custom operators, coming in addition to the five basic ones (+, -, X, / and √), but achieving better performance by treating more operations. This thesis addresses the design of such custom operators as well as the techniques developed in the compiler to select them in application codes. We have designed optimized implementations for a set of custom operators which includes squaring, scaling, adding two nonnegative terms, fused multiply-add, fused square-add (x*x+z, with z>=0), two-dimensional dot products (DP2), sums of two squares, as well as simultaneous addition/subtraction and sine/cosine. With novel algorithms targeting high instruction-level parallelism and detailed here for squaring, scaling, DP2, and sin/cos, we achieve speedups of up to 4.2x for individual custom operators even when subnormal numbers are fully supported. Furthermore, we introduce the optimizations developed in the ST231 C/C++ compiler for selecting such operators. Most of the selections are achieved at high level, using syntactic criteria. However, for fused square-add, we also enhance the framework of integer range analysis to support floating-point variables in order to prove the required positivity condition z>= 0. Finally, we provide quantitative evidence of the benefits to support this selection of custom operations: on DSP kernels and benchmarks, our approach allows us to be up to 1.59x faster compared to the sole usage of basic ones.

Les applications multimédia se composent généralement de blocs numériques exhibant des schémas de calcul flottant réguliers. Sur les processeurs sans support architectural pour l'arithmétique flottante, ils peuvent être profitablement transformés en opérateurs dédiés, s'ajoutant aux 5 opérateurs élémentaires (+, -, X, / et √) : en traitant plus d'opérations simultanément, ils permettent d'obtenir de meilleures performances. Cette thèse porte sur la conception de tels opérateurs, et les techniques de compilation mises en œuvre pour les sélectionner. Nous avons réalisé des implémentations optimisées pour un ensemble d'opérateurs dédiés : élévation au carré, mise à l'échelle, fused multiply-add, produit scalaire en dimension deux (DP2), addition/soustraction simultané et sinus/cosinus simultanés. En proposant de nouveaux algorithmes cherchant à maximiser le parallélisme d'instructions et détaillés ici, nous obtenons des accélérations d'un facteur allant jusqu'à 4.2 par appel. Nous détaillons également les changements apportés dans le compilateur pour effectuer la sélection. La plupart des opérateurs sont sélectionnés au niveau syntaxique. Cependant, pour certains opérateurs, nous avons dû améliorer l'analyse d'intervalles entiers pour prendre en compte les variables de type flottant, afin de prouver certaines conditions de positivité requises à leur sélection. Enfin, nous apportons la preuve en pratique de la pertinence de cette approche : sur des noyaux typiques du traitement du signal et sur certaines applications, nous mesurons une amélioration de performance allant jusqu'à 1.59x en comparaison avec la performance obtenue avec les seuls opérateurs élémentaires. / Media processing applications typically involve numerical blocks that exhibit regular floating-point computation patterns. For processors whose architecture supports only integer arithmetic, these patterns can be profitably turned into custom operators, coming in addition to the five basic ones (+, -, X, / and √), but achieving better performance by treating more operations. This thesis addresses the design of such custom operators as well as the techniques developed in the compiler to select them in application codes. We have designed optimized implementations for a set of custom operators which includes squaring, scaling, adding two nonnegative terms, fused multiply-add, fused square-add (x*x+z, with z>=0), two-dimensional dot products (DP2), sums of two squares, as well as simultaneous addition/subtraction and sine/cosine. With novel algorithms targeting high instruction-level parallelism and detailed here for squaring, scaling, DP2, and sin/cos, we achieve speedups of up to 4.2x for individual custom operators even when subnormal numbers are fully supported. Furthermore, we introduce the optimizations developed in the ST231 C/C++ compiler for selecting such operators. Most of the selections are achieved at high level, using syntactic criteria. However, for fused square-add, we also enhance the framework of integer range analysis to support floating-point variables in order to prove the required positivity condition z>= 0. Finally, we provide quantitative evidence of the benefits to support this selection of custom operations: on DSP kernels and benchmarks, our approach allows us to be up to 1.59x faster compared to the sole usage of basic ones.