1,811 research outputs found
Load-Varying LINPACK: A Benchmark for Evaluating Energy Efficiency in High-End Computing
For decades, performance has driven the high-end computing (HEC) community. However, as highlighted in recent exascale studies that chart a path from petascale to exascale computing, power consumption is fast becoming the major design constraint in HEC. Consequently, the HEC community needs to address this issue in future petascale and exascale computing systems.
Current scientific benchmarks, such as LINPACK and SPEChpc, only evaluate HEC systems when running at full throttle, i.e., 100% workload, resulting in a focus on performance and ignoring the issues of power and energy consumption. In contrast, efforts like SPECpower evaluate the energy efficiency of a compute server at varying workloads. This is analogous to evaluating the energy efficiency (i.e., fuel efficiency) of an automobile at varying speeds (e.g., miles per gallon highway versus city). SPECpower, however, only evaluates the energy efficiency of a single compute server rather than a HEC system; furthermore, it is based on SPEC's Java Business Benchmarks (SPECjbb) rather than a scientific benchmark. Given the absence of a load-varying scientific benchmark to evaluate the energy efficiency of HEC systems at different workloads, we propose the load-varying LINPACK (LV-LINPACK) benchmark. In this paper, we identify application parameters that affect performance and provide a methodology to vary the workload of LINPACK, thus enabling a more rigorous study of energy efficiency in supercomputers, or more generally, HEC
The Problem with the Linpack Benchmark Matrix Generator
We characterize the matrix sizes for which the Linpack Benchmark matrix
generator constructs a matrix with identical columns
The Green500 List: Escapades to Exascale
Energy efficiency is now a top priority. The first
four years of the Green500 have seen the importance of en-
ergy efficiency in supercomputing grow from an afterthought
to the forefront of innovation as we near a point where sys-
tems will be forced to stop drawing more power. Even so,
the landscape of efficiency in supercomputing continues to
shift, with new trends emerging, and unexpected shifts in
previous predictions.
This paper offers an in-depth analysis of the new and
shifting trends in the Green500. In addition, the analysis of-
fers early indications of the track we are taking toward exas-
cale, and what an exascale machine in 2018 is likely to look
like. Lastly, we discuss the new efforts and collaborations
toward designing and establishing better metrics, method-
ologies and workloads for the measurement and analysis of
energy-efficient supercomputing
Random circuit block-encoded matrix and a proposal of quantum LINPACK benchmark
The LINPACK benchmark reports the performance of a computer for solving a
system of linear equations with dense random matrices. Although this task was
not designed with a real application directly in mind, the LINPACK benchmark
has been used to define the list of TOP500 supercomputers since the debut of
the list in 1993. We propose that a similar benchmark, called the quantum
LINPACK benchmark, could be used to measure the whole machine performance of
quantum computers. The success of the quantum LINPACK benchmark should be
viewed as the minimal requirement for a quantum computer to perform a useful
task of solving linear algebra problems, such as linear systems of equations.
We propose an input model called the RAndom Circuit Block-Encoded Matrix
(RACBEM), which is a proper generalization of a dense random matrix in the
quantum setting. The RACBEM model is efficient to be implemented on a quantum
computer, and can be designed to optimally adapt to any given quantum
architecture, with relying on a black-box quantum compiler. Besides solving
linear systems, the RACBEM model can be used to perform a variety of linear
algebra tasks relevant to many physical applications, such as computing
spectral measures, time series generated by a Hamiltonian simulation, and
thermal averages of the energy. We implement these linear algebra operations on
IBM Q quantum devices as well as quantum virtual machines, and demonstrate
their performance in solving scientific computing problems.Comment: 22 pages, 18 figure
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