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Data encryption/decryption has become an essential part of modern information systems. However, executing these cryptography algorithms introduces high overhead issues for performance, power, and hardware cost. Through profiling work, we found that cryptography algorithms are data-intensive, and memory access overhead plays an important role in downgrading the performance. We found that the instructions...
Exascale computing requires complex runtime systems that need to consider affinity, load balancing and low time and message complexity for scheduling massive scale parallel computations. Simultaneous consideration of these objectives makes online distributed scheduling a very challenging problem. Prior distributed scheduling approaches are limited to shared memory or primarily use work-stealing across...
With many-core processor architectures emerging, concerns arise regarding the productivity of numerous parallel programming tools, models, and languages as developers from a broad spectrum of science domains struggle to maximize performance and maintain correctness of their applications. Fortunately, a partitioned global address space (PGAS) programming model has demonstrated realizable performance...
In large-scale networks where a continuously on-going monitoring program is needed, using traditional predicate detection algorithms might cause the system to have a single point of failure. This paper presents an on-line distributed algorithm that detects strong conjunctive predicates and we show that it is resilient to node failures. Our algorithm assumes a pre-constructed spanning tree in the system,...
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