Storage constraint satisfaction for embedded processor compilers

Increasing interest in the high-volume high-performance embedded processor market motivates the stand-alone processor world to consider issues like design flexibility (synthesizable processor core), energy consumption, and silicon efficiency. Implications for embedded processor architectures and compilers are the exploitation of hardware acceleration, instruction-level parallelism (ILP), and distributed storage files. In that scope, VLIW architectures have been acclaimed for their parallelism in the architecture while orthogonality of the associated instruction sets is maintained. Code generation methods for such processors will be pressured towards an efficient use of scarce resources while satisfying tight real-time constraints imposed by DSP and multimedia applications. Limited storage (e.g. registers) availability poses a problem for traditional methods that perform code generation in separate stages, e.g. operation scheduling followed by register allocation. This is because the objectives of scheduling and register allocation cause conflicts in code generation in several ways. Firstly, register reuse can create dependencies that did not exist in the original code, but can also save spilling values to memory. Secondly, while a particular ordering of instructions may increase the potential for ILP, the reordering due to instruction scheduling may also extend the lifetime of certain values, which can increase the register requirement. Furthermore, the instruction scheduler requires an adequate number of local registers to avoid register reuse (since reuse limits the opportunity for ILP), while the register allocator would prefer sufficient global registers in order to avoid spills. Finally, an effective scheduler can lose its achieved degree of instruction-level parallelism when spill code is inserted afterwards. Without any communication of information and cooperation between scheduling and storage allocation phases, the compiler writer faces the problem of determining which of these phases should run first to generate the most efficient final code. The lack of communication and cooperation between the instruction scheduling and storage allocation can result in code that contains excess of register spills and/or lower degree of ILP than actually achievable. This problem called phase coupling cannot be ignored when constraints are tight and efficient solutions are desired. Traditional methods that perform code generation in separate stages are often not able to find an efficient or even a feasible solution. Therefore, those methods need an increasing amount of help from the programmer (or designer) to arrive at a feasible solution. Because this requires an excessive amount of design time and extensive knowledge of the processor architecture, there is a need for automated techniques that can cope with the different kinds of constraints during scheduling. This thesis proposes an approach for instruction scheduling and storage allocation that makes an extensive use of timing, resource and storage constraints to prune the search space for scheduling. The method in this approach supports VLIW architectures with (distributed) storage files containing random-access registers, rotating registers to exploit the available ILP in loops, stacks or fifos to exploit larger storage capacities with lower addressing costs. Potential access conflicts between values are analyzed before and during scheduling, according to the type of storage they are assigned to. Using constraint analysis techniques and properties of colored conflict graphs essential information is obtained to identify the bottlenecks for satisfying the storage file constraints. To reduce the identified bottlenecks, this method performs partial scheduling by ordering value accesses such that to allow a better reuse of storage. Without enforcing any specific storage assignment of values, the method continues until it can guarantee that any completion of the partial schedule will also result in a feasible storage allocation. Therefore, the scheduling freedom is exploited for satisfaction of storage, resource, and timing constraints in one phase

Modulo scheduling with reduced register pressure

Software pipelining is a scheduling technique that is used by some product compilers in order to expose more instruction level parallelism out of innermost loops. Module scheduling refers to a class of algorithms for software pipelining. Most previous research on module scheduling has focused on reducing the number of cycles between the initiation of consecutive iterations (which is termed II) but has not considered the effect of the register pressure of the produced schedules. The register pressure increases as the instruction level parallelism increases. When the register requirements of a schedule are higher than the available number of registers, the loop must be rescheduled perhaps with a higher II. Therefore, the register pressure has an important impact on the performance of a schedule. This paper presents a novel heuristic module scheduling strategy that tries to generate schedules with the lowest II, and, from all the possible schedules with such II, it tries to select that with the lowest register requirements. The proposed method has been implemented in an experimental compiler and has been tested for the Perfect Club benchmarks. The results show that the proposed method achieves an optimal II for at least 97.5 percent of the loops and its compilation time is comparable to a conventional top-down approach, whereas the register requirements are lower. In addition, the proposed method is compared with some other existing methods. The results indicate that the proposed method performs better than other heuristic methods and almost as well as linear programming methods, which obtain optimal solutions but are impractical for product compilers because their computing cost grows exponentially with the number of operations in the loop body.Peer ReviewedPostprint (published version

Hypernode reduction modulo scheduling

Software pipelining is a loop scheduling technique that extracts parallelism from loops by overlapping the execution of several consecutive iterations. Most prior scheduling research has focused on achieving minimum execution time, without regarding register requirements. Most strategies tend to stretch operand lifetimes because they schedule some operations too early or too late. The paper presents a novel strategy that simultaneously schedules some operations late and other operations early, minimizing all the stretchable dependencies and therefore reducing the registers required by the loop. The key of this strategy is a pre-ordering that selects the order in which the operations will be scheduled. The results show that the method described in this paper performs better than other heuristic methods and almost as well as a linear programming method but requiring much less time to produce the schedules.Peer ReviewedPostprint (published version

A unified modulo scheduling and register allocation technique for clustered processors

This work presents a modulo scheduling framework for clustered ILP processors that integrates the cluster assignment, instruction scheduling and register allocation steps in a single phase. This unified approach is more effective than traditional approaches based on sequentially performing some (or all) of the three steps, since it allows optimizing the global code generation problem instead of searching for optimal solutions to each individual step. Besides, it avoids the iterative nature of traditional approaches, which require repeated applications of the three steps until a valid solution is found. The proposed framework includes a mechanism to insert spill code on-the-fly and heuristics to evaluate the quality of partial schedules considering simultaneously inter-cluster communications, memory pressure and register pressure. Transformations that allow trading pressure on a type of resource for another resource are also included. We show that the proposed technique outperforms previously proposed techniques. For instance, the average speed-up for the SPECfp95 is 36% for a 4-cluster configuration.Peer ReviewedPostprint (published version

Survey on Combinatorial Register Allocation and Instruction Scheduling

Register allocation (mapping variables to processor registers or memory) and instruction scheduling (reordering instructions to increase instruction-level parallelism) are essential tasks for generating efficient assembly code in a compiler. In the last three decades, combinatorial optimization has emerged as an alternative to traditional, heuristic algorithms for these two tasks. Combinatorial optimization approaches can deliver optimal solutions according to a model, can precisely capture trade-offs between conflicting decisions, and are more flexible at the expense of increased compilation time. This paper provides an exhaustive literature review and a classification of combinatorial optimization approaches to register allocation and instruction scheduling, with a focus on the techniques that are most applied in this context: integer programming, constraint programming, partitioned Boolean quadratic programming, and enumeration. Researchers in compilers and combinatorial optimization can benefit from identifying developments, trends, and challenges in the area; compiler practitioners may discern opportunities and grasp the potential benefit of applying combinatorial optimization

goSLP: Globally Optimized Superword Level Parallelism Framework

Modern microprocessors are equipped with single instruction multiple data (SIMD) or vector instruction sets which allow compilers to exploit superword level parallelism (SLP), a type of fine-grained parallelism. Current SLP auto-vectorization techniques use heuristics to discover vectorization opportunities in high-level language code. These heuristics are fragile, local and typically only present one vectorization strategy that is either accepted or rejected by a cost model. We present goSLP, a novel SLP auto-vectorization framework which solves the statement packing problem in a pairwise optimal manner. Using an integer linear programming (ILP) solver, goSLP searches the entire space of statement packing opportunities for a whole function at a time, while limiting total compilation time to a few minutes. Furthermore, goSLP optimally solves the vector permutation selection problem using dynamic programming. We implemented goSLP in the LLVM compiler infrastructure, achieving a geometric mean speedup of 7.58% on SPEC2017fp, 2.42% on SPEC2006fp and 4.07% on NAS benchmarks compared to LLVM's existing SLP auto-vectorizer.Comment: Published at OOPSLA 201

Swing modulo scheduling: a lifetime-sensitive approach

This paper presents a novel software pipelining approach, which is called Swing Modulo Scheduling (SMS). It generates schedules that are near optimal in terms of initiation interval, register requirements and stage count. Swing Modulo Scheduling is an heuristic approach that has a low computational cost. The paper describes the technique and evaluates it for the Perfect Club benchmark suite. SMS is compared with other heuristic methods showing that it outperforms them in terms of the quality of the obtained schedules and compilation time. SMS is also compared with an integer linear programming approach that generates optimum schedules but with a huge computational cost, which makes it feasible only for very small loops. For a set of small loops, SMS obtained the optimum initiation interval in all the cases and its schedules required only 5% more registers and a 1% higher stage count than the optimumPeer ReviewedPostprint (published version

Modulo scheduling with integrated register spilling for clustered VLIW architectures

Clustering is a technique to decentralize the design of future wide issue VLIW cores and enable them to meet the technology constraints in terms of cycle time, area and power dissipation. In a clustered design, registers and functional units are grouped in clusters so that new instructions are needed to move data between them. New aggressive instruction scheduling techniques are required to minimize the negative effect of resource clustering and delays in moving data around. In this paper we present a novel software pipelining technique that performs instruction scheduling with reduced register requirements, register allocation, register spilling and inter-cluster communication in a single step. The algorithm uses limited backtracking to reconsider previously taken decisions. This backtracking provides the algorithm with additional possibilities for obtaining high throughput schedules with low spill code requirements for clustered architectures. We show that the proposed approach outperforms previously proposed techniques and that it is very scalable independently of the number of clusters, the number of communication buses and communication latency. The paper also includes an exploration of some parameters in the design of future clustered VLIW cores.Peer ReviewedPostprint (published version

A systematic integration of register allocation and instruction scheduling

In order to achieve high performance, processor architecture has become more and more complicated. As a result, compiler-time optimizations have become more and more important for the effective use of a complex processor. One of the promising compiler-time optimizations is the integration of register allocation and instruction scheduling based on register-reuse chains. In the previous approach, however, the generation of register-reuse chains was not completely systematic and consequently created many unnecessary dependencies that restrict instruction scheduling. This research proposes a new register allocation technique based on a systematic generation of register-reuse chains. The first phase of the proposed technique is to generate register-reuse chains that are optimal in the sense that no additional dependencies are created. Thus, register allocation can be done without restricting instruction scheduling. For the case when the optimal register-reuse chains require more than available registers, the second phase reduces the number of required registers by merging the register-reuse chains. A heuristic is developed for the second phase in order to reduce the additional dependencies created by merging chains. The first step of the second phase is to derive a conflict graph in which each node corresponds to a register-reuse chain, while an edge represents where the corresponding two chains cannot be merged. Applying a graph-coloring algorithm to the conflict graph, the number of chains can be effectively reduced. The final step of the second phase is to run the 0-1 knapsack algorithm to make the number of chains exactly the same as the number of available registers. The proposed register allocation is implemented in LCC (Local C Compiler). An instruction scheduler is also implemented in LCC and then integrated with the proposed register allocator. Evaluation results show that the proposed algorithm and heuristic effectively reduce the number of necessary registers

Operations research methods in compiler backends

Operations research can be defined as the theory of numerically solving decision problems. In this context, dealing with optimization problems is a central issue. Code generation is performed by the backend phase of a compiler, a program which transforms the source code of an application into optimized machine code. Basically, code generation is an optimization problem, which can be modelled in a way similar to typical problems in the area of operations research. In this article, that similarity is demonstrated by opposing integer linear programming models for problems of the operations research and of code generation. The time frame for solving the generated integer linear programs (ILPs) as a part of the compilation process is small. As a consequence, using well-structured ILP-formulations and ILP-based approximations is necessary. The second part of the paper will give a brief survey on guidelines and techniques for both issues