Data dependency size estimation for use in memory optimization

Kjeldsberg, Per Gunnar; Catthoor, Francky; Aas, E.J.

doi:10.1109/tcad.2003.814257

Cited by 37 publications

(26 citation statements)

References 31 publications

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“…Interestingly, the possibility of memory sharing between elements of different arrays with disjoint lifetimes was observed long time ago (e.g., [10]) and it has been taken into account by several approaches for memory size evaluation (e.g., [11,12]). The memory sharing is sometimes called "inter-array in-place mapping" when the elements belong to different arrays, and "intra-array in-place mapping" when the elements belong to the same array [3,11].…”

Section: Introductionmentioning

confidence: 99%

“…The memory sharing is sometimes called "inter-array in-place mapping" when the elements belong to different arrays, and "intra-array in-place mapping" when the elements belong to the same array [3,11]. It must be emphasized that these terms can create confusion since they do not necessary refer to signal-to-memory mapping techniques, where an explicit correspondence between the array elements and their addresses in the physical memory is indicated; they rather refer to the possible reuse of data storage by array elements having disjoint lifetimes.…”

Section: Introductionmentioning

confidence: 99%

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Signal Assignment Model for the Memory Management of Multidimensional Signal Processing Applications

Balasa

Luican

Zhu

et al. 2009

J Sign Process Syst

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Many signal processing systems, particularly in the multimedia and telecom domains, are synthesized to execute data-dominated applications. Their behavior is described in a high-level 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 cost-optimized 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 both the point of view of computational efficiency and the amount of allocated data storage. Different from all the previous mapping models that aim to optimize the memory sharing between the elements of a same array (creating separate windows in the physical memory for distinct arrays), this proposed mapping model exploitin a second phase-the possibility of memory sharing between the elements of different arrays. As a consequence, this signal assignment approach yields significant savings in the amount of data storage resulted after mapping.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Signal Assignment Model for the Memory Management of Multidimensional Signal Processing Applications

Balasa

Luican

Zhu

et al. 2009

J Sign Process Syst

View full text Add to dashboard Cite

show abstract

“…The estimation technique described in [5] a partially fixed execution ordering. The authors employ a data dependence analysis similar to [1], their major improvement being to add the capability of taking into account available execution ordering information, based mainly on loop interchanges.…”

Section: Introductionmentioning

confidence: 99%

Computation of the Minimum Data Storage for Multi-Dimensional Signal Processing

Luican

Zhu

Balasa

2007

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07

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-The amount of the data storage in signal processing systems, whose behavior is described by loop-organized algorithmic specifications, has an important impact on the overall energy consumption, chip area, as well as system performance. This paper presents a non-scalar approach for computing the minimum storage requirements in high-level procedural specifications, where the main data structures are multi-dimensional arrays. This methodology uses both algebraic techniques specific to the data-flow analysis used in modern compilers and, also, more recent advances in the theory of polyhedra. In contrast with all the previous works which are only estimation methods, this approach can perform the exact computation of the minimum data storage even for applications with numerous loop nests and complex array references.

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“…These counts are used to drive optimizations such as increasing parallelism [38], minimizing memory size [1,2,27,38,42], estimating worst case execution time [28], increasing cache effectiveness [6,16], high-level transformations for DSP applications [23], converting software loops into parallel hardware implementations [5,18,22,25,38] and minimizing communication overhead in distributed applications [9,26,37]. In many of these optimizations, the objects or events to be counted are modeled as the integer solutions to systems of linear inequalities, i.e., as the elements of a set S = { x ∈ Z d | Ax + c ≥ 0 }, with A ∈ Z n×d and c ∈ Z n .…”

Section: Introductionmentioning

confidence: 99%

Experiences with Enumeration of Integer Projections of Parametric Polytopes

Verdoolaege

Beyls

Bruynooghe

et al. 2005

Lecture Notes in Computer Science

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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.

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Data dependency size estimation for use in memory optimization

Cited by 37 publications

References 31 publications

Signal Assignment Model for the Memory Management of Multidimensional Signal Processing Applications

Signal Assignment Model for the Memory Management of Multidimensional Signal Processing Applications

Computation of the Minimum Data Storage for Multi-Dimensional Signal Processing

Experiences with Enumeration of Integer Projections of Parametric Polytopes

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