Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 91 for “"Data Movement"”.
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Optimizing network data movement in server class microprocessors
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01
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Parallel sorting and Star-P data movement and tree flattening
This thesis studies three problems in the field of parallel computing. The first result provides a deterministic parallel sorting algorithm that empirically shows an improvement over two sample sort algorithms. When using a comparison sort, this algorithm is 1-optimal in both computation and …
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Threat Detection in Program Execution and Data Movement: Theory and Practice
… threats. They compromise the confidentiality of data, the integrity of program logic, and the availability of services. This threat becomes even severer when followed by other malicious activities such as data exfiltration. The integration of primitive attacks constructs comprehensive attack …
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An integrated transport solution to big data movement in high-performance networks
… the U.S. are generating colossal amounts of data, now frequently termed as "big data". The big data must be stored, managed and moved to different geographical locations for distributed data processing and analysis. Such big data transfers require stable and high-speed network connections, …
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Reducing data movement in multicore chips with computation and data co-scheduling
… focus on making cores more efficient. However, data movement is much more costly than basic compute operations. For example, at 28 nm, a main memory access is 100x slower and consumes 1000x the energy of a floatingpoint operation, and moving 64 bytes across a 16-core processor is 50 x slower and …
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Optimizing data movement in cloud-bursting HPC environments through dynamic labeling and prefetching strategies
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01
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Energy-Efficient On-Chip Cache Architectures and Deep Neural Network Accelerators Considering the Cost of Data Movement
付記する学位プログラム名: 京都大学卓越大学院プログラム「先端光・電子デバイス創成学」
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Image alignment and dynamic graph analytics : two case studies of how managing data movement can make (parallel) code run fast
… want to solve more complex problems and use more data to get higher quality results. However, the more data we store, the slower it is to access any piece. This effect is seen directly in the memory hierarchy. We can access our caches faster than our memory, which is faster than reading our disk, …
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Design and analysis of spatially-partitioned shared caches
Data movement is a growing problem in modern chip-multiprocessors (CMPs). Processors spend the majority of their time, energy, and area moving data, not processing it. For example, a single main memory access takes hundreds of cycles and costs the energy of a thousand floating-point operations. …
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Designing a Programmable Hardware Accelerator for Fully Homomorphic Encryption
… Encryption (FHE) allows computing on encrypted data, enabling secure offloading of computation to untrusted servers. Though it provides ideal security, FHE is expensive when executed in software, 4 to 5 orders of magnitude slower than computing on unencrypted data. These overheads are a major …
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External Memory Algorithms for Factoring Sparse Matrices
… becomes out-of-core, which means that data movement and computation must be interleaved.</p> <p>We identify two major out-of-core factorization scenarios: read-once/write-once (R1/W1) and read-many/write-many (RM/WM). The former requires minimum traffic, exactly as much as the in-core …
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Scalability Analysis and Optimization for Large-Scale Deep Learning
… by many factors, including those deriving from data movement and data processing. DL frameworks rely on large volumes of data to be fed to the computation engines for processing. However, current hardware trends showcase that data movement is already one of the slowest components in modern high …
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Making Computation on Encrypted Data Practical through Hardware Acceleration of Fully Homomorphic Encryption
… provides so much compute throughput that data movement becomes the key bottleneck. Thus, F1 is primarily designed to minimize data movement. It speeds up shallow FHE computations (i.e., those of limited multiplicative depth) by gmean 5,400× over a 4-core CPU. Unfortunately, F1 becomes …
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Heterogeneous system and application communication modeling
… it has exacerbated existing challenges of data placement as the specialized hardware often has local memories to fuel its computational demands. In addition to using appropriate software resources to target application computation at the best hardware for the job, application developers now …
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Techniques to improve dynamic cache management with static data classification
… use static information about how programs access data to manage the memory hierarchy. Static techniques are effective on regular programs, but because they set fixed policies, they are vulnerable to changes in program behavior or available cache space. Instead, most systems rely on dynamic caching …
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Programming High-Performance Clusters with Heterogeneous Computing Devices
… model and runtime system for efficient data movement and automatic task mapping across the CPUs and accelerators within a cluster, and discuss the lessons learned. MPI-ACC's task-mapping runtime subsystem performs fast and automatic device selection for a given task. MPI-ACC's …
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SERIAL PROTOCOL BRIDGE
… these devices are becoming more and more data centric. Apart from data storage another major concern in such scenarios is data movement. Many protocols exist to aid in data movement within the mobile devices as well as to communicate with the outside world. Among these protocols serial …
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Systems support for genomics computing in cloud environments
… the availability of the unprecedented genomic data. However, new sequencing technologies in genomics keep producing data at a faster pace resulting a very huge amount of data. This poses great challenges on how to store, manage, process and analyze the data efficiently. To deal with these, …
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