Above are papers related to the following paper: [Paper_ID] Paper #235670. 	[Title] Improving Software Pipelining With Unroll-and-Jam [Abstract] In this paper, we demonstrate how unroll-and-jam can significantly improve the initiation interval in a software-pipelined loop. Improvements in the initiation interval of greater than 40% are common, while dramatic improvements of a factor of 5 are possible. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #235683. 	[Title] Minimizing Register Requirements under Resource-Constrained Rate-Optimal Software Pipelining [Abstract] In this paper we address the following software pipelin-ing problem: given a loop and a machine architecture with a fixed number of processor resources (e.g. function units), how can one construct a software-pipelined schedule which runs on the given architecture at the maximum possible iteration rate (a la rate-optimal) while minimizing the number of registers? The main contributions of this paper are: * First, we demonstrate that such problem can be described by a simple mathematical formulation with precise optimization objectives under periodic linear scheduling framework. The mathematical formulation provides a clear picture which permits one to visualize the overall solution space (for rate-optimal schedules) under different sets of con straints. * Secondly, we show that a precise mathematical formulation and its solution does make a significant performance difference! We evaluated the performance of our method against three other leading contemporary heuristic methods: Huff 's Slack Scheduling [9], Wang, Eisenbeis, Jourdan and Su's FRLC [23], and Gasperoni and Schwiegelshohn's modified list scheduling [6]. Experimental results show that the method described in this paper performed significantly better than these methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #689439. 	[Title] A Reduced Multipipeline Machine Description that Preserves Scheduling Constraints [Abstract] High performance compilers increasingly rely on accurate modeling of the machine resources to efficiently exploit the instruction level parallelism of an application. In this paper, we propose a reduced machine description that results in faster detection of resource contentions while preserving the scheduling constraints present in the original machine description. The proposed approach reduces a machine description in an automated, error-free, and efficient fashion. Moreover, it fully supports schedulers that backtrack and process operations in arbitrary order. Reduced descriptions for the DEC Alpha 21064, MIPS R3000/R3010, and Cydra 5 result in 4 to 7 times faster detection of resource contentions and require 22 to 90% of the memory storage used by the original machine descriptions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #57773. 	[Title] Scheduling and Mapping: Software Pipelining in the Presence of Structural Hazards proposed formulation and a [Abstract] Recently, software pipelining methods based on an ILP (Integer Linear Programming) framework have been successfully applied to derive rate-optimal schedules for architectures involving clean pipelines | pipelines without structural hazards. The problem for architectures beyond such clean pipelines remains open. One challenge is how, under a unified ILP framework, to simultaneously represent resource constraints for unclean pipelines, and the assignment or mapping of operations from a loop to those pipelines. In this paper we provide a framework which does exactly this, and in addition constructs rate-optimal software pipelined schedules. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1154524. 	[Title] Abstract [Abstract] This paper is a scientific comparison of two code generation techniques with identical goals generation of the best possible software pipelined code for computers with instruction level parallelism. Both are variants of modulo scheduling, a framework for generation of software pipelines pioneered by Rau and Glaser [RaGl81], but are otherwise quite dissimilar. One technique was developed at Silicon Graphics and is used in the MIPSpro compiler. This is the production compiler for SGI s systems which are based on the MIPS R8000 processor [Hsu94]. It is essentially a branchandbound enumeration of possible schedules with extensive pruning. This method is heuristic because of the way it prunes and also because of the interaction between register allocation and scheduling. 1 The second technique aims to produce optimal results by formulating the scheduling and register allocation problem as an integrated integer linear programming (ILP 1 ) problem. This idea has received much recent exposure in the literature [AlGoGa95, Feautrier94, GoAlGa94a, GoAlGa94b, Eichenberger95], but to our knowledge all previous implementations have been too preliminary for detailed measurement and evaluation. In particular, we believe this to be the first published measurement of runtime performance for ILP based generation of software pipelines. A particularly valuable result of this study was evaluation of the heuristic pipelining technology in the SGI compiler . One of the motivations behind the McGill research was the hope that optimal software pipelining, while not in itself practical for use in production compilers, would be useful for their evaluation and validation. Our comparison has indeed provided a quantitative validation of the SGI compilers pipeliner, leading us to increased confidence in both techniques. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #235678. 	[Title] Stage Scheduling: A Technique to Reduce the Register Requirements of a Modulo Schedule [Abstract] Modulo scheduling is an efficient technique for exploiting instruction level parallelism in a variety of loops, resulting in high performance code but increased register requirements. We present a set of low computational complexity stage-scheduling heuristics that reduce the register requirements of a given modulo schedule by shifting operations by multiples of II cycles. Measurements on a benchmark suite of 1289 loops from the Perfect Club, SPEC-89, and the Livermore Fortran Kernels shows that our best heuristic achieves on average 99% of the decrease in register requirements obtained by an optimal stage scheduler. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #235679. 	[Title] Minimum Register Requirements for a Modulo Schedule [Abstract] Modulo scheduling is an efficient technique for exploiting instruction level parallelism in a variety of loops, resulting in high performance code but increased register requirements. We present a combined approach that schedules the loop operations for minimum register requirements, given a modulo reservation table. Our method determines optimal register requirements for machines with finite resources and for general dependence graphs. This method demonstrates the potential of lifetime-sensitive modulo scheduling and is useful in evaluating the performance of lifetime-sensitive modulo scheduling heuristics. Please summarise all the given information.

Paper #235670 has the title `Improving Software Pipelining With Unroll-and-Jam  
` and the abstract `In this paper, we demonstrate how unroll-and-jam can significantly improve the initiation interval in a software-pipelined loop. Improvements in the initiation interval of greater than 40% are common, while dramatic improvements of a factor of 5 are possible. 
`.
Question: Which category should Paper #235670 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #235670 has the title `Improving Software Pipelining With Unroll-and-Jam  
` and the abstract `In this paper, we demonstrate how unroll-and-jam can significantly improve the initiation interval in a software-pipelined loop. Improvements in the initiation interval of greater than 40% are common, while dramatic improvements of a factor of 5 are possible. 
`.
Question: Which category should Paper #235670 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Rule_Learning