Robin L. Michael v. Rodney C. Michael : Brief of Appellant

ON APPEAL FROM THE THIRD JUDICIAL DISTRICT COURT IN AND FOR SALT LAKE COUNTY, STATE OF UTAH HONORABLE TIMOTHY R. HANSON, DISTRICT COURT JUDG

Analysis of scratch-pad and data-cache performance using statistical methods

Abstract—An effectively designed and efficiently used memory hierarchy, composed of scratch-pads or cache, is seen today as the key to obtaining energy and performance gains in data-dominated embedded applications. However, an unsolved problem is – how to make the right choice between the scratch-pad and the data-cache for different class of applications? Recent studies show that applications with regular and manifest data access patterns (e.g. matrix multiplication) perform better on the scratch-pad compared to the cache. In the case of dynamic applications with irregular and non-manifest access patterns, it is however commonly and intuitively believed that the cache would perform better. In this paper, we show by theoretical analysis and empirical results that this intuition can sometimes be misleading. When access-probabilities remain fixed, we prove that the scratch-pad, with an optimal mapping, will always outperform the cache. We also demonstrate how to map dynamic applications efficiently to scratch-pad or cache and additionally, how to accurately predict the performance. I

Experiences with enumeration of integer projections of parametric polytopes

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

Array size computation under uniform overlapping and irregular accesses

The size required to store an array is crucial for an embedded system, as it affects the memory size, the energy per memory access, and the overall system cost. Existing techniques for finding the minimum number of resources required to store an array are less efficient for codes with large loops and not regularly occurring memory accesses. They have to approximate the accessed parts of the array leading to overestimation of the required resources. Otherwise, their exploration time is increased with an increase over the number of the different accessed parts of the array. We propose a methodology to compute the minimum resources required for storing an array which keeps the exploration time low and provides a near-optimal result for regularly and non-regularly occurring memory accesses and overlapping writes and reads