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James Lebak


Click on the titles below to find US government-authored or -collected reports written by James Lebak

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Kernel Benchmarks and Metrics for Polymorphous Computer Architectures 20 AUG 2004
Authors:  James Lebak; Hank Hoffmann; Janice McMahon; Eddie Rutledge; Glenn Schrader; MASSACHUSETTS INST OF TECH LEXINGTON LINCOLN LAB
The full text of this report is not available and therefore is not for sale. This information is provided for reference purposes only.Polymorphous computer architectures (PCA) are new computer architectures being developed under a DARPA/IPTO program to support mission agility for future high performance DoD embedded applications. These new architectures will have the ability to "morph" into different modes of execution with the goal of delivering uniform, high performance across a large variety of different processing types and workload compositions. Examples of these architectures include the MIT RAW machine, the Stanford Smart ...


Mapping Signal Processing Kernels to Tiled Architectures 05 MAY 2004
Authors:  Henry Hoffmann; James Lebak; MASSACHUSETTS INST OF TECH LEXINGTON LINCOLN LAB
The full text of this report is not available and therefore is not for sale. This information is provided for reference purposes only.This presentation focuses on understanding when a stream algorithm exists for a given kernel. We do so by considering the directed acyclic graph (DAG) for a particular implementation of the kernel. Nodes in the DAG represent inputs, outputs, or intermediate products of the algorithm, and edges from node A to node B in the DAG show that A is used to compute B. We can characterize the DAG for an ...


Implementing the Matrix Exponential Function on Embedded Processors 22 APR 2004
Authors:  James Lebak; Andrea Wadell; MASSACHUSETTS INST OF TECH LEXINGTON LINCOLN LAB
The full text of this report is not available and therefore is not for sale. This information is provided for reference purposes only.In this paper we describe and benchmark an implementation of the matrix exponential function. The implementation is based on the standard technique of scaling and squaring from the literature. The major kernels in this technique are matrix multiplication and Gaussian elimination. In the matrix multiply kernel, the implementation makes use of SIMD vector extensions present on the PowerPC G4 (Altivec) and the Intel Xeon (SSE-2). Although the use of the ...


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