A study of scalar compilation techniques for pipelined supercomputers

This paper studies two compilation techniques for enhancing scalar performance in high-speed scientific processors: software pipelining and loop unrolling. We study the impact of the architecture (size of the register file) and of the hardware (size of instruction buffer) on the efficiency of loop unrolling. We also develop a methodology for classifying software pipelining techniques. For loop unrolling, a straightforward scheduling algorithm is shown to produce near-optimal results when not inhibited by recurrences or memory hazards. Our study indicates that the performance produced with a modified CRAY-1S scalar architecture and a code scheduler utilizing loop unrolling is comparable to the performance achieved by the CRAY-1S with a vector unit and the CFT vectorizing compiler. Finally, we show that the combination of loop unrolling and dynamic software pipelining, as implemented by a decoupled computer, substantially outperforms the vector CRAY-1S.

This paper studies two compilation techniques for enhancing scalar performance in high-speed scientific processors: software pipelining and loop unrolling. We study the impact of the architecture (size of the register file) and of the hardware (size of instruction buffer) on the efficiency of loop unrolling. We also develop a methodology for classifying software pipelining techniques. For loop unrolling, a straightforward scheduling algorithm is shown to produce near-optimal results when not inhibited by recurrences or memory hazards. Software pipelining requires less hardware but also achieves less speedup. Finally, we show that the performance produced with a modified CRAY-1S scalar architecture and a code scheduler utilizing loop unrolling is comparable to the performance achieved by the CRAY-1S with a vector unit and the CFT vectorizing compiler.

This paper studies two compilation techniques for enhancing scalar performance in high-speed scientific processors: software pipelining and loop unrolling. We study the impact of the architecture (size of the register file) and of the hardware (size of instruction buffer) on the efficiency of loop unrolling. We also develop a methodology for classifying software pipelining techniques. For loop unrolling, a straightforward scheduling algorithm is shown to produce near-optimal results when not inhibited by recurrences or memory hazards. Software pipelining requires less hardware but also achieves less speedup. Finally, we show that the performance produced with a modified CRAY-1S scalar architecture and a code scheduler utilizing loop unrolling is comparable to the performance achieved by the CRAY-1S with a vector unit and the CFT vectorizing compiler.

A considerable part of program execution time is consumed by loops, so that loop optimization is highly effective especially for the innermost loops of a program. Software pipelining and loop unrolling are known methods for loop optimization. Software pipelining is advantageous in that the code becomes only slightly longer. This method, however, is difficult to apply if the loop includes branching when the parallelism is limited. On the other hand, loop unrolling, while being free of such limitations, suffers from a number of drawbacks. In particular the code size grows substantially and it is difficult to determine the optimal number of body replications. In order to solve these problems, it seems important to combine software pipelining with loop unrolling so as to utilize the advantages of both techniques while paying due regard to properties of programs under consideration and to the machine resources available. This paper describes a method for applying optimal loop unrolling and effective software pipelining to achieve this goal. Program characteristics obtained by means of an extended PDG (program dependence graph) are taken into consideration as well as machine resources. © 1998 Scripta Technica, Syst Comp Jpn, 29(9): 62–73, 1998

This paper improves our previous research effort [1] by providing an efficient method for kernel loop unrolling minimisation in the case of already scheduled loops, where circular lifetime intervals are known. When loops are software pipelined, the number of values simultaneously alive becomes exactly known giving better opportunities for kernel loop unrolling. Furthermore, fixing circular lifetime intervals allows us to reduce the algorithmic complexity of our method compared to [1] by computing a new research space for minimal kernel loop unrolling. The meeting graph (MG) is one of the [3] frameworks proposed in the literature which models loop unrolling and register allocation together in a common formal framework for software pipelined loops. Although MG significantly improves loop register allocation, the computed loop unrolling may lead to unpractical code growth. This work proposes to minimise the loop unrolling degree in the meeting graph by making an adaptation of [1] the approach described in . We explain how to reduce the research space for minimal kernel loop unrolling in the context of MG, yielding to a reduced algorithmic complexity. Furthermore, our experiments on SPEC2000, SPEC2006, MEDIABENCH and FFMPEG show that in concrete cases the loop unrolling minimisation is very fast and the minimal loop unrolling degree for 75% of the optimised loops is equal to 1 (i.e. no unroll), while it is equal to 7 when the software pipelining (SWP) schedule is not fixed.

We address the problem of generating compact code from software pipelined loops. Although software pipelining is a powerful technique to extract fine-grain parallelism, it generates lifetime intervals spanning multiple loop iterations. These intervals require periodic register allocation (also called variable expansion), which in turn yields a code generation challenge. We are looking for the minimal unrolling factor enabling the periodic register allocation of software pipelined kernels. This challenge is generally addressed through one of: (1) hardware support in the form of rotating register files, which solve the unrolling problem but are expensive in hardware; (2) register renaming by inserting register moves, which increase the number of operations in the loop, and may damage the schedule of the software pipeline and reduce throughput; (3) post-pass loop unrolling that does not compromise throughput but often leads to impractical code growth. The latter approach relies on the proof that MAXLIVE registers (maximal number of values simultaneously alive) are sufficient for periodic register allocation (Eisenbeis et al. in PACT ’95: Proceedings of the IFIP WG10.3 working conference on Parallel Architectures and Compilation Techniques, pages 264–267, Manchester, UK, 1995; Hendren et al. in CC ’92: Proceedings of the 4th International Conference on Compiler Construction, pages 176–191, London, UK, 1992). However, the best existing heuristic for controlling this code growth—modulo variable expansion (Lam in SIGPLAN Not 23(7):318–328, 1988)—may not apply the correct amount of loop unrolling to guarantee that MAXLIVE registers are enough, which may result in register spills Eisenbeis et al. in PACT ’95: Proceedings of the IFIP WG10.3 working conference on Parallel Architectures and Compilation Techniques, pages 264–267, Manchester, UK, 1995. This paper presents our research results on the open problem of minimal loop unrolling, allowing a software-only code generation that does not trade the optimality of the initiation interval (II) for the compactness of the generated code. Our novel idea is to use the remaining free registers after periodic register allocation to relax the constraints on register reuse. The problem of minimal loop unrolling arises either before or after software pipelining, either with a single or with multiple register types (classes). We provide a formal problem definition for each scenario, and we propose and study a dedicated algorithm for each problem. Our solutions are implemented within an industrial-strength compiler for a VLIW embedded processor from STMicroelectronics, and validated on multiple benchmarks suites.

Software pipelining is a powerful technique to expose fine-grain parallelism, but it results in variables staying alive across more than one kernel iteration. It requires periodic register allocation and is challenging for code generation: the lack of a reliable solution currently restricts the applicability of software pipelining. The classical software solution that does not alter the computation throughput consists in unrolling the loop a posteriori [11], [10]. However, the resulting unrolling degree is often unacceptable and may reach absurd levels. Alternatively, loop unrolling can be avoided thanks to software register renaming. This is achieved through the insertion of move operations, but this may increase the initiation interval (II) which nullifies the benefits of software pipelining. This article aims at tightly controling the post-pass loop unrolling necessary to generate code. We study the potential of live range splitting to reduce kernel loop unrolling, introducing additional move instructions without inscreasing the II. We provide a complete formalisation of the problem, an algorithm, and extensive experiments. Our algorithm yields low unrolling degrees in most cases - with no increase of the II.

Recently loop unrolling has been shown in a new light from the superscalar architectural point of view. In this paper, we show that in addition to superscalar effect and scalar replacement effect, loop unrolling can hide memory latency, and that the combination of those effects improve the performance of loop unrolling. A major contribution of this paper is that the analysis is done symbolically and quantitatively. Although they have been known as major reasons that affect the performance of loop unrolling, no quantitative approach has not been tried. Our analysis can make clear the behaviour of superscalar functions and memory latency hiding in loop unrolling.

Software oriented techniques to hide memory latency in superscalar and superpipelined machines include loop unrolling, software pipelining, and software cache prefetching. Issuing the data fetch request prior to actual need for data allows overlap of accessing with useful computations. Loop unrolling and software pipelining do not necessitate microarchitecture or instruction set architecture changes, whereas software controlled prefetching does. While studies on the benefits of the individual techniques have been done, no study evaluates all of these techniques within a consistent framework. This paper attempts to remedy this by providing a comparative evaluation of the features and benefits of the techniques. Loop, unrolling and static scheduling of loads is seen to produce significant improvement in performance at lower latencies. Software pipelining is observed to be better than software controlled prefetching at lower latencies, but at higher latencies, software prefetching outperforms software pipelining. Aggressive prefetching beyond conditional branches can detrimentally affect performance by increasing the memory bandwidth requirements and bus traffic. >

Programmable media processors have been emerging to meet the continuously increasing computational demand in complex digital media applications, such as HDTV and MPEG-4, at an afford- able cost. These media processors provide the flexibility to imple- ment various image computing algorithms along with high perfor- mance, unlike the hardwired approach that has provided high performance for a particular algorithm, but lacks flexibility. However, to achieve high performance on these media processors, a careful and sometimes innovative design of algorithms is essential. In ad- dition, programming techniques, e.g., software pipelining and loop unrolling, are needed to speed up the computations while the data flow can be optimized using a programmable DMA controller. In this paper, we describe an algorithm for two-dimensional convolution, which can be implemented efficiently on many media processors. Implemented on a new media processor called the MAP1000, it takes 7.9 ms to convolve a 5123512 image with a 737 kernel, which is much faster than the previously reported software-based convolution and is comparable with the hardwired implementations. High performance in two-dimensional convolution and other algo- rithms on the MAP1000 clearly demonstrates the feasibility of software-based solutions in demanding imaging and video applica- tions. © 2000 SPIE and IS&T. (S1017-9909(00)00203-8)

: Many of today's high-performance computer processors are super-scalar. They can dispatch multiple instructions per cycle and, hence, provide what is commonly referred to as instruction-level parallelism. This super-scalar capability, combined with software pipelining, can increase processor throughput dramatically. Achieving maximum throughput, however, is nontrivial. Compilers must engage in aggressive optimization techniques, such as loop unrolling, speculative code motion, etc., to structure code to take full advantage of the underlying computer architecture. The phase-ordering implications of these optimizations are not well understood and are the subject of continuing research. Of particular interest are optimizations that enhance instruction-level parallelism. Two of these are loop unrolling and loop fusion. These are source-level optimizations that can be performed by either the programmer or the compiler. These optimizations have dramatic effects on the compiler's instruction scheduler. Performed too aggressively, these optimizations can increase register pressure and result in costly memory references. This paper details experiments performed to measure the effects of these source-level code transformations and how they influenced register pressure and code performance.

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Enhanced pipeline scheduling (EPS) is a software pipelining technique which can achieve a variable initiation interval (II) for loops with control flow via its code motion pipelining. EPS, however, leaves behind many renaming copy instructions that cannot be coalesced due to interferences. These copies take resources and, more seriously, they may cause a stall if they rename a multilatency instruction whose latency is longer than the II aimed for by EPS. This paper proposes a code transformation technique based on loop unrolling which makes those copies coalescible. Two unique features of the technique are its method of determining the precise unroll amount, based on an idea of extended live ranges, and its insertion of special bookkeeping copies at loop exits. The proposed technique enables EPS to avoid a serious slowdown from latency handling and resource pressure, while keeping its variable II and other advantages. In fact, renaming through copies, followed by unroll-based copy elimination, is EPS's solution to the cross-iteration register overwrite problem in software pipelining. It works for loops with arbitrary control flow that EPS must deal with, as well as for straightline loops. Our empirical study performed on a VLIW testbed with a two-cycle load latency shows that 86 percent of the otherwise uncoalescible copies in innermost loops become coalescible when unrolled 2.2 times on average. In addition, it is demonstrated that the unroll amount obtained is precise and the most efficient. The unrolled version of the VLIW code includes fewer no-op VLIW caused by stalls, improving the performance by a geometric mean of 18 percent on a 16-ALU machine.

Article Free Access Share on Techniques for low energy software Authors: Huzefa Mehta Equator Technologies, Cambell, CA Equator Technologies, Cambell, CAView Profile , Robert Michael Owens Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PA Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PAView Profile , Mary Jane Irwin Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PA Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PAView Profile , Rita Chen Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PA Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PAView Profile , Debashree Ghosh Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PA Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PAView Profile Authors Info & Claims ISLPED '97: Proceedings of the 1997 international symposium on Low power electronics and designAugust 1997 Pages 72–75https://doi.org/10.1145/263272.263286Online:01 August 1997Publication History 86citation1,008DownloadsMetricsTotal Citations86Total Downloads1,008Last 12 Months36Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

EPIC architectures rely heavily on state-of-the-art compiler technology to deliver optimal performance while keeping hardware design simple. It is generally believed that an optimizing compiler has an enormous scheduling window to exploit instruction-level parallelism (ILP) since the compiler orchestrates the entire program. Many state-of-the-art compilers typically confine optimizations to loop boundaries (e.g. software pipelining, trace scheduling, and loop unrolling) and function boundaries (e.g. loop peeling, loop exchanges, invariant hoisting, and global optimizations). Although techniques such as function inlining and interprocedural optimizations can alleviate these constraints to a limited extent, loop and function boundaries are often the real scopes of the compiler scheduler. Several previous ILP studies have explored the limits of parallelism on dynamic superscalar machines; however, those results are not applicable to EPIC architectures since they rely on dynamic scheduling, not static code scheduling by the compiler, to reorder instructions. In this paper, we evaluate the limits in ILP obtained through compiler scheduling alone. We quantify these limits as more restrictive scheduling constraints are imposed-starting from inter-procedural code scheduling, to intra-procedural and finally to loop-confined code scheduling.