201 research outputs found
The End of Slow Networks: It's Time for a Redesign
Next generation high-performance RDMA-capable networks will require a
fundamental rethinking of the design and architecture of modern distributed
DBMSs. These systems are commonly designed and optimized under the assumption
that the network is the bottleneck: the network is slow and "thin", and thus
needs to be avoided as much as possible. Yet this assumption no longer holds
true. With InfiniBand FDR 4x, the bandwidth available to transfer data across
network is in the same ballpark as the bandwidth of one memory channel, and it
increases even further with the most recent EDR standard. Moreover, with the
increasing advances of RDMA, the latency improves similarly fast. In this
paper, we first argue that the "old" distributed database design is not capable
of taking full advantage of the network. Second, we propose architectural
redesigns for OLTP, OLAP and advanced analytical frameworks to take better
advantage of the improved bandwidth, latency and RDMA capabilities. Finally,
for each of the workload categories, we show that remarkable performance
improvements can be achieved
TensorFlow Doing HPC
TensorFlow is a popular emerging open-source programming framework supporting
the execution of distributed applications on heterogeneous hardware. While
TensorFlow has been initially designed for developing Machine Learning (ML)
applications, in fact TensorFlow aims at supporting the development of a much
broader range of application kinds that are outside the ML domain and can
possibly include HPC applications. However, very few experiments have been
conducted to evaluate TensorFlow performance when running HPC workloads on
supercomputers. This work addresses this lack by designing four traditional HPC
benchmark applications: STREAM, matrix-matrix multiply, Conjugate Gradient (CG)
solver and Fast Fourier Transform (FFT). We analyze their performance on two
supercomputers with accelerators and evaluate the potential of TensorFlow for
developing HPC applications. Our tests show that TensorFlow can fully take
advantage of high performance networks and accelerators on supercomputers.
Running our TensorFlow STREAM benchmark, we obtain over 50% of theoretical
communication bandwidth on our testing platform. We find an approximately 2x,
1.7x and 1.8x performance improvement when increasing the number of GPUs from
two to four in the matrix-matrix multiply, CG and FFT applications
respectively. All our performance results demonstrate that TensorFlow has high
potential of emerging also as HPC programming framework for heterogeneous
supercomputers.Comment: Accepted for publication at The Ninth International Workshop on
Accelerators and Hybrid Exascale Systems (AsHES'19
High Performance Computing using Infiniband-based clusters
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