A Hyperplane Based Approach for Optimizing Spatial Locality in Loop Nests

This paper presents a data layout optimization technique based on the theory of hyperplanes from linear algebra. Given a program, our framework automatically determines the optimal layouts that can be expressed by hyperplanes for each array that is referenced. We discuss the cases where data transformations are preferable to loop transformations and show that under specific conditions a loop nest can be optimized for perfect spatial locality by using data transformations. We divide the problem of optimizing data layout into two independent subproblems: (1) determining optimal layouts, and (2) determining data transformation matrices to implement optimal layouts. By postponing the determination of the transformation matrix to the last stage, our method can be adapted to compilers with different default layouts. Our results on eight programs on SGI Origin 2000 distributed-shared-memory multiprocessor show that the layout optimizations are effective in optimizing spatial locality

A Hyperplane Based Approach for Optimizing Spatial Locality in Loop Nests

This paper presents a data layout optimization technique based on the theory of hyperplanes from linear algebra. Given a program, our framework automatically determines the optimal layouts that can be expressed by hyperplanes for each array that is referenced. We discuss the cases where data transformations are preferable to loop transformations and show that under specific conditions a loop nest can be optimized for perfect spatial locality by using data transformations. We divide the problem of optimizing data layout into two independent subproblems: (1) determining optimal layouts, and (2) determining data transformation matrices to implement optimal layouts. By postponing the determination of the transformation matrix to the last stage, our method can be adapted to compilers with different default layouts. Our results on eight programs on SGI Origin 2000 distributed-shared-memory multiprocessor show that the layout optimizations are effective in optimizing spatial locality.

Optimal caching of large multi-dimensional datasets

We propose a novel organization for multi-dimensional data based on the conceptof macro-voxels. This organization improves computer performance by enhancingspatial and temporal locality. Caching of macro-voxels not only reduces therequired storage space but also leads to an efficient organization of the dataset resulting in faster data access. We have developed a macro-voxel caching theory that predicts the optimal macro-voxel sizes required for minimum cache size and access time. The model also identifies a region of trade-off between time and storage, which can be exploited in making an efficient choice of macro-voxel size for this scheme. Based on the macro-voxel caching model, we have implemented a macro-voxel I/O layer in C, intended to be used as an interface between applications and datasets. It is capable of both scattered access, typical in online applications, and row/column access, typical in batched applications. We integrated this I/O layer in the ALIGN program (online application) which aligns images based on 3D distance maps; this improved access time by a factor of 3 when accessing local disks and a factor of 20 for remote disks. We also applied the macro-voxel caching scheme on SPEC.s Seismic (batched application) benchmark datasets which improved the read process by a factor of 8.Ph.D., Electrical and Computer Engineering -- Drexel University, 200