Percolation scheduling with resource constraints

This paper presents a new approach to resource-constrained compiler extraction of fine-grain parallelism, targeted towards VLIW supercomputers, and in particular, the IBM VLIW (Very Large Instruction Word) processor. The algorithms described integrate resource limitations into Percolation Scheduling—a global parallelization technique—to deal with resource constraints, without sacrificing the generality and completeness of Percolation Scheduling in the process. This is in sharp contrast with previous approaches which either applied only to conditional-free code, or drastically limited the parallelization process by imposing relatively local heuristic resource constraints early in the scheduling process

Percolation scheduling for non-VLIW machines

Percolation Scheduling, a technique for compile-time code parallelization, has proven very successful for exploiting fine-grain irregular parallelism in ordinary programs. Currently, this technology is targeted only to VLIW (Very Long Instruction Word) machines, which have the advantages of 'free' synchronization and communication. Shared memory multi-processors can simulate the execution characteristics of VLIW machines with the use of static barriers. Preliminary results show that Percolation Scheduling can be used with good results on this type of architecture by increasing the granularity from operation level to source statement level, removing any redundant synchronization, and providing an efficient implementation of multi-way jumps

An efficient global resource constrained technique for exploiting instruction level parallelism

A new Global Resource-constrained Percolation (GRiP) scheduling technique is presented for exploiting instruction level parallelism. Other techniques that have been proposed either have been prohibitively expensive in terms of computation or have limited parallelism. The GRiP technique has been implemented and simulation results are presented

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

Incremental tree height reduction for code compaction

This paper introduces a new Tree Height Reduction (THR) technique for code compaction. THR, which is well known parallelizing method, has two interesting properties: while known compilation techniques can get constant factor of speed-up, THR has speed-up of O(n/logn). Furthermore, THR is able to compact code which seems, at first, uncompactable (due to data dependencies). The algorithm presented is incremental, local (so in each step, it is checking the the current operation and its predecessor rather than the whole expression tree to see whether compaction is possible) and applicable beyond basic block limits. THR is applied after all other optimization techniques, none of which change the semantics of the code, have been applied. THR is changing the semantics of the code, thus preserving, of course, the correctness of the intermediate and final values. Also, the reduction is controlled according to the resources available - so in case the compaction is feasible but there are not enough resources - it moves to the next operation. The algorithm produces compacted code suited for any tightly coupled multiprocessors (e.g. Very Long Instruction Word {or VLIW) machines). To our knowledge, it is the first local and incremental THR algorithm working across basic blocks boundaries published so far for code compaction

An Algebra of Synchronous Scheduling Interfaces

In this paper we propose an algebra of synchronous scheduling interfaces which combines the expressiveness of Boolean algebra for logical and functional behaviour with the min-max-plus arithmetic for quantifying the non-functional aspects of synchronous interfaces. The interface theory arises from a realisability interpretation of intuitionistic modal logic (also known as Curry-Howard-Isomorphism or propositions-as-types principle). The resulting algebra of interface types aims to provide a general setting for specifying type-directed and compositional analyses of worst-case scheduling bounds. It covers synchronous control flow under concurrent, multi-processing or multi-threading execution and permits precise statements about exactness and coverage of the analyses supporting a variety of abstractions. The paper illustrates the expressiveness of the algebra by way of some examples taken from network flow problems, shortest-path, task scheduling and worst-case reaction times in synchronous programming.Comment: In Proceedings FIT 2010, arXiv:1101.426

Coarse-grained reconfigurable array architectures

Coarse-Grained Reconfigurable Array (CGRA) architectures accelerate the same inner loops that benefit from the high ILP support in VLIW architectures. By executing non-loop code on other cores, however, CGRAs can focus on such loops to execute them more efficiently. This chapter discusses the basic principles of CGRAs, and the wide range of design options available to a CGRA designer, covering a large number of existing CGRA designs. The impact of different options on flexibility, performance, and power-efficiency is discussed, as well as the need for compiler support. The ADRES CGRA design template is studied in more detail as a use case to illustrate the need for design space exploration, for compiler support and for the manual fine-tuning of source code