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... The course covers the fundamentals of data structures and object-oriented programming. They are two sides of the same coin: – As a programmer becomes more proficient, they realize that how well and efficiently a problem can be solved often depends on how the data are stored. Some of the ideas are qu ...
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Introduction (in ) - ECE Concordia

... Primary components - Central Processor (ALU and Control), Memory (Primary and Secondary), and Input/Output Memory:  Primary storage – all programs (instructions) are loaded before they are executed » Instructions and data stored as sequence of bits » Main memories are usually volatile but accessed ...
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... OLAP queries are characterized by short answering times. Materialized cube views, a pre-aggregation and storage of group-by values, are one of the possible answers to that condition. However, if all possible views were computed and stored, the amount of necessary materializing time and storage space ...
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Goals of this lecture Let us assume … Human Brain – No Problem!

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... To compute e5 using 20 terms, we’d call (sum-series e-to-the-x 5 20). Since the stream represents a function, we can write operations which work on functions and try to implement them in terms of the coefficients of the series. One such operation is integration. The integral of an infinite polynomia ...
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Stream processing

Stream processing is a computer programming paradigm, equivalent to data-flow programming and reactive programming, that allows some applications to more easily exploit a limited form of parallel processing. Such applications can use multiple computational units, such as the FPUs on a GPU or field programmable gate arrays (FPGAs), without explicitly managing allocation, synchronization, or communication among those units.The stream processing paradigm simplifies parallel software and hardware by restricting the parallel computation that can be performed. Given a set of data (a stream), a series of operations (kernel functions) is applied to each element in the stream. Uniform streaming, where one kernel function is applied to all elements in the stream, is typical. Kernel functions are usually pipelined, and local on-chip memory is reused to minimize external memory bandwidth. Since the kernel and stream abstractions expose data dependencies, compiler tools can fully automate and optimize on-chip management tasks. Stream processing hardware can use scoreboarding, for example, to launch DMAs at runtime, when dependencies become known. The elimination of manual DMA management reduces software complexity, and the elimination of hardware caches reduces the amount of the area not dedicated to computational units such as ALUs.During the 1980s stream processing was explored within dataflow programming. An example is the language SISAL (Streams and Iteration in a Single Assignment Language).
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