Simulation of DigiCipher, an HDTV system proposal

Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 1991.Thesis (Master's) -- Bilkent University, 1991.Includes bibliographical references leaves 51In this thesis, the digital video encoder-decoder parts of an American HDTV system proposal, DigiCipher^^'^ is simulated in an image sequencer, based on the system description sheets. Numerical and subjective performances are tested, by observing and making calculations on the decoder outputs of the system simulation. The performance tests show that the image quality does not have HDTV quality. Considering the very good picture quality in the demonstrations of the designer company (General Instruments), it is suspected that the description sheets do not mention all of the data compression methods used in the system.Öktem, LeventM.S

SparCML: High-Performance Sparse Communication for Machine Learning

Applying machine learning techniques to the quickly growing data in science and industry requires highly-scalable algorithms. Large datasets are most commonly processed "data parallel" distributed across many nodes. Each node's contribution to the overall gradient is summed using a global allreduce. This allreduce is the single communication and thus scalability bottleneck for most machine learning workloads. We observe that frequently, many gradient values are (close to) zero, leading to sparse of sparsifyable communications. To exploit this insight, we analyze, design, and implement a set of communication-efficient protocols for sparse input data, in conjunction with efficient machine learning algorithms which can leverage these primitives. Our communication protocols generalize standard collective operations, by allowing processes to contribute arbitrary sparse input data vectors. Our generic communication library, SparCML, extends MPI to support additional features, such as non-blocking (asynchronous) operations and low-precision data representations. As such, SparCML and its techniques will form the basis of future highly-scalable machine learning frameworks

Performance evaluation of word-aligned compression methods for bitmap indices

Bitmap indices are a widely used scheme for large read-only repositories in data warehouses and scientific databases. This binary representation allows the use of bit-wise operations for fast query processing and is typically compressed using run-length encoding techniques. Most bitmap compression techniques are aligned using a fixed encoding length (32 or 64 bits) to avoid explicit decompression during query time. They have been proposed to extend or enhance word-aligned hybrid (WAH) compression. This paper presents a comparative study of four bitmap compression techniques: WAH, PLWAH, CONCISE, and EWAH. Experiments are targeted to identify the conditions under which each method should be applied and quantify the overhead incurred during query processing. Performance in terms of compression ratio and query time is evaluated over synthetic-generated bitmap indices, and results are validated over bitmap indices generated from real data sets. Different query optimizations are explored, query time estimation formulas are defined, and the conditions under which one method should be preferred over another are formalized