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21
Synthesis of HighPerformance Parallel Programs for a Class of Ab Initio Quantum Chemistry Models
 PROCEEDINGS OF THE IEEE
, 2005
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Counting integer points in parametric polytopes using Barvinok’s rational functions
 Algorithmica
, 2007
"... Abstract Many compiler optimization techniques depend on the ability to calculate the number of elements that satisfy certain conditions. If these conditions can be represented by linear constraints, then such problems are equivalent to counting the number of integer points in (possibly) parametric ..."
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Cited by 44 (9 self)
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Abstract Many compiler optimization techniques depend on the ability to calculate the number of elements that satisfy certain conditions. If these conditions can be represented by linear constraints, then such problems are equivalent to counting the number of integer points in (possibly) parametric polytopes. It is well known that the enumerator of such a set can be represented by an explicit function consisting of a set of quasipolynomials each associated with a chamber in the parameter space. Previously, interpolation was used to obtain these quasipolynomials, but this technique has several disadvantages. Its worstcase computation time for a single quasipolynomial is exponential in the input size, even for fixed dimensions. The worstcase size of such a quasipolynomial (measured in bits needed to represent the quasipolynomial) is also exponential in the input size. Under certain conditions this technique even fails to produce a solution. Our main contribution is a novel method for calculating the required quasipolynomials analytically. It extends an existing method, based on Barvinok’s decomposition,
Experiences with enumeration of integer projections of parametric polytopes
, 2004
"... Abstract. Many compiler optimization techniques depend on the ability to calculate the number of integer values that satisfy a given set of linear constraints. This count (the enumerator of a parametric polytope) is a function of the symbolic parameters that may appear in the constraints. In an ex ..."
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Cited by 20 (7 self)
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Abstract. Many compiler optimization techniques depend on the ability to calculate the number of integer values that satisfy a given set of linear constraints. This count (the enumerator of a parametric polytope) is a function of the symbolic parameters that may appear in the constraints. In an extended problem (the “integer projection ” of a parametric polytope), some of the variables that appear in the constraints may be existentially quantified and then the enumerated set corresponds to the projection of the integer points in a parametric polytope. This paper shows how to reduce the enumeration of the integer projection of parametric polytopes to the enumeration of parametric polytopes. Two approaches are described and experimentally compared. Both can solve problems that were considered very difficult to solve analytically. 1
Computation of storage requirements for multidimensional signal processing applications
 IEEE TRANS. ON VLSI SYSTEMS
, 2007
"... Many integrated circuit systems, particularly in the multimedia and telecom domains, are inherently data dominant. For this class of systems, a large part of the power consumption is due to the data storage and data transfer. Moreover, a significant part of the chip area is occupied by memory. The c ..."
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Cited by 12 (8 self)
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Many integrated circuit systems, particularly in the multimedia and telecom domains, are inherently data dominant. For this class of systems, a large part of the power consumption is due to the data storage and data transfer. Moreover, a significant part of the chip area is occupied by memory. The computation of the memory size is an important step in the systemlevel exploration, in the early stage of designing an optimized (for area and/or power) memory architecture for this class of systems. This paper presents a novel nonscalar approach for computing exactly the minimum size of the data memory for highlevel procedural specifications of multidimensional signal processing applications. In contrast with all the previous works which are estimation methods, this approach can perform exact memory computations even for applications with numerous and complex array references, and also with large numbers of scalars.
Memory size computation for multimedia processing applications
 Proc. Asia & SouthPacific Design Automation Conf
, 2006
"... Abstract – In realtime multimedia processing systems a very large part of the power consumption is due to the data storage and data transfer. Moreover, the area cost is often largely dominated by the memory modules. The computation of the memory size is an important step in the process of designing ..."
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Cited by 7 (4 self)
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Abstract – In realtime multimedia processing systems a very large part of the power consumption is due to the data storage and data transfer. Moreover, the area cost is often largely dominated by the memory modules. The computation of the memory size is an important step in the process of designing an optimized (for area and/or power) memory architecture for multimedia processing systems. This paper presents a novel nonscalar approach for computing exactly the memory size in realtime multimedia algorithms. This methodology uses both algebraic techniques specific to the dataflow analysis used in modern compilers, and also recent advances in the theory of integral polyhedra. In contrast with all the previous works which are only estimation methods, this approach performs exact memory computations even for applications with a large number of scalar signals. 1
Loop Transformation Methodologies for ArrayOriented Memory Management
 IEEE 17th International Conference on Applicationspecific Systems, Architectures and Processors (ASAP 2006), Steamboat
, 2006
"... Abstract – The storage requirements in datadominant signal processing systems, whose behavior is described by arraybased, looporganized algorithmic specifications, have an important impact on the overall energy consumption, data access latency, and chip area. Applying different loop transformation ..."
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Cited by 6 (0 self)
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Abstract – The storage requirements in datadominant signal processing systems, whose behavior is described by arraybased, looporganized algorithmic specifications, have an important impact on the overall energy consumption, data access latency, and chip area. Applying different loop transformations on the specification code can significantly enhance the memory management of such VLSI systems, improving all the major parameters of the design space – power, area, and performance. This paper gives a global view on existing and recently proposed memory size evaluation approaches for procedural and nonprocedural specifications. Moreover, it discusses typical memory management tradeoffs taken into account during the exploration of system specifications by loop transformations, that can exploit these early size evaluations. 1
Memory requirement optimization with loop fusion and loop shifting
 In Euromicro Symp. on Digital System Design (DSD’04
, 2004
"... Loop fusion and loop shifting are well recognized loop transformations for memory requirement reduction. Stateoftheart optimizations with loop fusion and shifting are based on heuristics without any evaluation of the resulting effects during each optimization step. Thus we cannot guarantee that ea ..."
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Cited by 2 (1 self)
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Loop fusion and loop shifting are well recognized loop transformations for memory requirement reduction. Stateoftheart optimizations with loop fusion and shifting are based on heuristics without any evaluation of the resulting effects during each optimization step. Thus we cannot guarantee that each step results in a reduced overall memory requirement. On the other hand, most memory requirement estimations at system level are inefficient and slow. Also the estimation is not started until the optimization is done. Having to iterate between optimization and estimation is very time consuming. In this paper, we present a storage requirement optimization method which combines the optimization and estimation processes with the goal to have continuous estimates during the optimization and hence to achieve lower memory requirements. 1.
Computation of memory requirements for multidimensional signal processing applications
, 2006
"... Many integrated circuit systems, particularly in the multimedia and telecom domains, are inherently data dominant. For this class of systems, a large part of the power consumption is due to the data storage and data transfer. Moreover, the area cost is often largely dominated by memory. Hence, the ..."
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Cited by 1 (0 self)
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Many integrated circuit systems, particularly in the multimedia and telecom domains, are inherently data dominant. For this class of systems, a large part of the power consumption is due to the data storage and data transfer. Moreover, the area cost is often largely dominated by memory. Hence, the optimization of the memory architecture is crucial in the design methodology for this type of applications. In deriving an optimized memory architecture, memory size computation is an important step in the exploration of the possible algorithmic specifications of multimedia applications. This thesis presents a novel nonscalar approach for computing exactly the memory size in realtime multidimensional signal processing algorithms, like telecom and multimedia applications. This methodology uses both algebraic techniques specific to the dataflow analysis used in modern compilers, and also recent advances in the theory of rational polyhedra. In contrast with all the previous works which are only estimation methods, this approach performs exact memory computations even for applications with numerous and complex array references.
Signal Assignment Model for the Memory Management of Multidimensional Signal Processing Applications
, 2011
"... Many signal processing systems, particularly in the multimedia and telecom domains, are synthesized to execute datadominated applications. Their behavior is described in a highlevel programming language, where the code is typically organized in sequences of loop nests and the main data structures ..."
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Cited by 1 (1 self)
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Many signal processing systems, particularly in the multimedia and telecom domains, are synthesized to execute datadominated applications. Their behavior is described in a highlevel programming language, where the code is typically organized in sequences of loop nests and the main data structures are multidimensional arrays. Since data transfer and storage have a significant impact on both the system performance and the major cost parameters—power consumption and chip area, the designer must spend a significant effort during the system development process on the exploration of the memory subsystem in order to achieve a costoptimized design. This paper presents a memory allocation methodology for multidimensional signal processing applications, focusing on the problem of efficiently mapping the multidimensional signals from the algorithmic specification into the physical memory. In a first phase, two previous mapping models are implemented within a common theoretical framework, which is advantageous from
EFFICIENT ASSIGNMENT ALGORITHM FOR MAPPING MULTIDIMENSIONAL SIGNALS INTO THE PHYSICAL MEMORY
, 2008
"... The storage requirements in dataintensive multidimensional signal processing systems have a significant impact on the system performance as well as on essential design parameters, like the overall power consumption and chip area. This paper addresses the problem of efficiently mapping the multidime ..."
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Cited by 1 (1 self)
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The storage requirements in dataintensive multidimensional signal processing systems have a significant impact on the system performance as well as on essential design parameters, like the overall power consumption and chip area. This paper addresses the problem of efficiently mapping the multidimensional signals from the algorithmic specification of the system into the physical memory. Different from all the previous mapping models that aim to optimize the memory sharing between the elements of a same array, this proposed assignment algorithm takes also into account the possibility of memory sharing between different arrays. As a consequence, the experiments with this novel signaltomemory mapping approach exhibit important savings of data storage resulted after mapping.