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Showing 1 to 7 of 7 for “"GPU applications"”.

  1. Intelligent scheduling for simultaneous CPU-GPU applications

    … has emerged recently. Graphics processing unit (GPU) is the most widely used accelerator. To fully utilize such a heterogeneous system’s full computing power, coordination between the two distinct devices, CPU and GPU, is necessary. Previous research has addressed this issue of partitioning the …

    uiuc Repository record for Intelligent scheduling for simultaneous CPU-GPU applications (opens in a new tab)

  2. Characterization of containers in emerging applications: Microservices, FAAS and GPUS

    … simple management and isolation of containerized applications. Docker is currently the most prominent container framework. This thesis utilizes Docker containers to create data center use cases with databases, web servers, graph analytics, Functions-as-a-Service, and GPU-accelerated stencil, …

    uiuc Repository record for Characterization of containers in emerging applications: Microservices, FAAS and GPUS (opens in a new tab)

  3. Architecture-Aware Mapping and Optimization on Heterogeneous Computing Systems

    The emergence of scientific applications embedded with multiple modes of parallelism has made heterogeneous computing systems indispensable in high performance computing. The popularity of such systems is evident from the fact that three out of the top five fastest supercomputers in the world …

    vt Repository record for Architecture-Aware Mapping and Optimization on Heterogeneous Computing Systems (opens in a new tab)

  4. Reducing Cache Contention On GPUs

    The usage of Graphics Processing Units (GPUs) as an application accelerator has become increasingly popular because, compared to traditional CPUs, they are more cost-effective, their highly parallel nature complements a CPU, and they are more energy efficient. With the popularity of GPUs, many …

    mississippi Repository record for Reducing Cache Contention On GPUs (opens in a new tab)

  5. Energy efficient computing exploiting data similarity and computation redundancy

    Applications in various fields, such as machine learning, scientific computing and signal/image processing, need to deal with real-world input datasets. Such input datasets are usually discrete samples of slow-changing, continuous data of physical phenomena, like temperature maps and images. Due to …

    uiuc Repository record for Energy efficient computing exploiting data similarity and computation redundancy (opens in a new tab)

  6. Type-2 Fuzzy Alpha-cuts

    … have not been implemented in real world applications unlike the astonishing number of applications involving standard fuzzy sets. The main reason behind this is the complex mathematical nature of type-2 fuzzy sets which is the source of two major problems. On one hand, it is difficult to …

    de-montfort Repository record for Type-2 Fuzzy Alpha-cuts (opens in a new tab)

  7. Advanced Optimization Techniques For Monte Carlo Simulation On Graphics Processing Units

    … to design and implement a self-adaptive parallel GPU optimized Monte Carlo algorithm for the simulation of adsorption in porous materials. We focus on Nvidia's GPUs and CUDA's Fermi architecture specifically. The resulting package supports the different ensemble methods for the Monte Carlo …

    wayne-thes Repository record for Advanced Optimization Techniques For Monte Carlo Simulation On Graphics Processing Units (opens in a new tab)