The DHRY program performs the dhrystone benchmarks on the 8051.
Dhrystone is a general-performance benchmark test originally
developed by Reinhold Weicker in 1984. This benchmark is
used to measure and compare the performance of different
computers or, in this case, the efficiency of the code
generated for the same computer by different compilers.
The test reports general performance in dhrystones per second.
Like most benchmark programs, dhrystone consists of standard
code and concentrates on string handling. It uses no
floating-point operations. It is heavily influenced by
hardware and software design, compiler and linker options,
code optimizing, cache memory, wait states, and integer
data types.
The DHRY program is available in different targets:
simulator: Large Model: DHRY example in LARGE model
for Simulation
Philips 80C51MX: DHRY example in LARGE model
for the Philips 80C51MC
標(biāo)簽：
general-performanc
benchmarks
Dhrystone
dhrystone
上傳時(shí)間：
2016-11-30
上傳用戶：hphh
Computational models are commonly used in engineering design and scientific discovery activities for simulating
complex physical systems in disciplines such as fluid mechanics, structural dynamics, heat transfer, nonlinear
structural mechanics, shock physics, and many others. These simulators can be an enormous aid to engineers who
want to develop an understanding and/or predictive capability for complex behaviors typically observed in the
corresponding physical systems. simulators often serve as virtual prototypes, where a set of predefined system
parameters, such as size or location dimensions and material properties, are adjusted to improve the performance
of a system, as defined by one or more system performance objectives. Such optimization or tuning of the
virtual prototype requires executing the simulator, evaluating performance objective(s), and adjusting the system
parameters in an iterative, automated, and directed way. System performance objectives can be formulated, for
example, to minimize weight, cost, or defects; to limit a critical temperature, stress, or vibration response; or
to maximize performance, reliability, throughput, agility, or design robustness. In addition, one would often
like to design computer experiments, run parameter studies, or perform uncertainty quantification (UQ). These
approaches reveal how system performance changes as a design or uncertain input variable changes. Sampling
methods are often used in uncertainty quantification to calculate a distribution on system performance measures,
and to understand which uncertain inputs contribute most to the variance of the outputs.
A primary goal for Dakota development is to provide engineers and other disciplinary scientists with a systematic
and rapid means to obtain improved or optimal designs or understand sensitivity or uncertainty using simulationbased
models. These capabilities generally lead to improved designs and system performance in earlier design
stages, alleviating dependence on physical prototypes and testing, shortening design cycles, and reducing product
development costs. In addition to providing this practical environment for answering system performance questions,
the Dakota toolkit provides an extensible platform for the research and rapid prototyping of customized
methods and meta-algorithms
標(biāo)簽：
Optimization and Uncertainty Quantification
上傳時(shí)間：
2016-04-08
上傳用戶：huhu123456
