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Computing systems have become increasingly heterogeneous contributing to higher performance and power efficiency. However, this is at the cost of increasing the overall complexity of designing such systems. One key challenge in the design of heterogeneous systems is the efficient scheduling of computational load. To address this challenge, this paper thoroughly analyzes state of the art scheduling...
The increased use of application-specific computational devices turns even low-power chips into high-performance computers. Not only additional accelerators (e.g., GPU, DSP, or even FPGA), but also heterogeneous CPU clusters form modern computer systems. Programming these chips is however challenging, due to management overhead, data transfer delays, and a missing unification of the programming flow...
Some of the newer processor architectures are no longer based on registers in order to increase their potential of instruction-level parallelism. Instead, they expose their data paths to the compiler so that the program is able to directly move data values between function units using suitable instructions. Some of these architectures require a synchronous transfer of data values while others use...
Reproducibility of the execution of scientific applications on parallel and distributed systems is a growing concern, underlying the trustworthiness of the experiments and the conclusions derived from experiments. Dynamic loop scheduling (DLS) techniques are an effective approach towards performance improvement of scientific applications via load balancing. These techniques address algorithmic and...
In this paper, we study the problem of vehicle scheduling in urban public transport systems taking into account the vehicle-type (different capacity and operating cost) known as VTSP. It is modeled as a multiobjective optimization problem (MOP). We propose a heuristic based on MOCell (Multi-Objective Cellular evolutionary algorithm) to solve the problem considering restrictions of government agencies...
Power is a primary concern for mobile, cloud, and high-performance computing applications. Approximate computing refers to running applications to obtain results with tolerable errors under resource constraints, and it can be applied to balance energy consumption with service quality. In this paper, we propose a "Good Enough (GE)" scheduling algorithm that uses approximate computing to provide...
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