4 research outputs found
10 Years Later: Cloud Computing is Closing the Performance Gap
Can cloud computing infrastructures provide HPC-competitive performance for
scientific applications broadly? Despite prolific related literature, this
question remains open. Answers are crucial for designing future systems and
democratizing high-performance computing. We present a multi-level approach to
investigate the performance gap between HPC and cloud computing, isolating
different variables that contribute to this gap. Our experiments are divided
into (i) hardware and system microbenchmarks and (ii) user application proxies.
The results show that today's high-end cloud computing can deliver
HPC-competitive performance not only for computationally intensive applications
but also for memory- and communication-intensive applications - at least at
modest scales - thanks to the high-speed memory systems and interconnects and
dedicated batch scheduling now available on some cloud platforms
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Evaluating the networking characteristics of the Cray XC-40 Intel Knights Landing-based Cori supercomputer at NERSC
There are many potential issues associated with deploying the Intel Xeon PhiTM (code named Knights Landing [KNL]) manycore processor in a large-scale supercomputer. One in particular is the ability to fully utilize the high-speed communications network, given that the serial performance of a Xeon PhiTM core is a fraction of a XeonÂźcore. In this paper, we take a look at the trade-offs associated with allocating enough cores to fully utilize the Aries high-speed network versus cores dedicated to computation, eg, the trade-off between MPI and OpenMP. In addition, we evaluate new features of Cray MPI in support of KNL, such as internode optimizations. We also evaluate one-sided programming models such as Unified Parallel C. We quantify the impact of the above trade-offs and features using a suite of National Energy Research Scientific Computing Center applications
Evaluating the networking characteristics of the Cray XC-40 Intel Knights Landing-based Cori supercomputer at NERSC
There are many potential issues associated with deploying the Intel Xeon PhiTM (code named Knights Landing [KNL]) manycore processor in a large-scale supercomputer. One in particular is the ability to fully utilize the high-speed communications network, given that the serial performance of a Xeon PhiTM core is a fraction of a XeonÂźcore. In this paper, we take a look at the trade-offs associated with allocating enough cores to fully utilize the Aries high-speed network versus cores dedicated to computation, eg, the trade-off between MPI and OpenMP. In addition, we evaluate new features of Cray MPI in support of KNL, such as internode optimizations. We also evaluate one-sided programming models such as Unified Parallel C. We quantify the impact of the above trade-offs and features using a suite of National Energy Research Scientific Computing Center applications