435,024 research outputs found
Optimal realizations of floating-point implemented digital controllers with finite word length considerations.
The closed-loop stability issue of finite word length (FWL) realizations is
investigated for digital controllers implemented in floating-point arithmetic.
Unlike the existing methods which only address the effect of the mantissa bits
in floating-point implementation to the sensitivity of closed-loop stability,
the sensitivity of closed-loop stability is analysed with respect to both the
mantissa and exponent bits of floating-point implementation. A computationally
tractable FWL closed-loop stability measure is then defined, and the method of
computing the value of this measure is given. The optimal controller realization
problem is posed as searching for a floating-point realization that maximizes
the proposed FWL closed-loop stability measure, and a numerical optimization
technique is adopted to solve for the resulting optimization problem. Simulation
results show that the proposed design procedure yields computationally efficient
controller realizations with enhanced FWL closed-loop stability performance
Optimistic Parallelization of Floating-Point Accumulation
Floating-point arithmetic is notoriously non-associative due to the limited precision representation which demands intermediate values be rounded to fit in the available precision. The resulting cyclic dependency in floating-point accumulation inhibits parallelization of the computation, including efficient use of pipelining. In practice, however, we observe that floating-point operations are "mostly" associative. This observation can be exploited to parallelize floating-point accumulation using a form of optimistic concurrency. In this scheme, we first compute an optimistic associative approximation to the sum and then relax the computation by iteratively propagating errors until the correct sum is obtained. We map this computation to a network of 16 statically-scheduled, pipelined, double-precision floating-point adders on the Virtex-4 LX160 (-12) device where each floating-point adder runs at 296 MHz and has a pipeline depth of 10. On this 16 PE design, we demonstrate an average speedup of 6Ć with randomly generated data and 3-7Ć with summations extracted from Conjugate Gradient benchmarks
Parallel Algorithms for Summing Floating-Point Numbers
The problem of exactly summing n floating-point numbers is a fundamental
problem that has many applications in large-scale simulations and computational
geometry. Unfortunately, due to the round-off error in standard floating-point
operations, this problem becomes very challenging. Moreover, all existing
solutions rely on sequential algorithms which cannot scale to the huge datasets
that need to be processed.
In this paper, we provide several efficient parallel algorithms for summing n
floating point numbers, so as to produce a faithfully rounded floating-point
representation of the sum. We present algorithms in PRAM, external-memory, and
MapReduce models, and we also provide an experimental analysis of our MapReduce
algorithms, due to their simplicity and practical efficiency.Comment: Conference version appears in SPAA 201
Certifying floating-point implementations using Gappa
High confidence in floating-point programs requires proving numerical
properties of final and intermediate values. One may need to guarantee that a
value stays within some range, or that the error relative to some ideal value
is well bounded. Such work may require several lines of proof for each line of
code, and will usually be broken by the smallest change to the code (e.g. for
maintenance or optimization purpose). Certifying these programs by hand is
therefore very tedious and error-prone. This article discusses the use of the
Gappa proof assistant in this context. Gappa has two main advantages over
previous approaches: Its input format is very close to the actual C code to
validate, and it automates error evaluation and propagation using interval
arithmetic. Besides, it can be used to incrementally prove complex mathematical
properties pertaining to the C code. Yet it does not require any specific
knowledge about automatic theorem proving, and thus is accessible to a wide
community. Moreover, Gappa may generate a formal proof of the results that can
be checked independently by a lower-level proof assistant like Coq, hence
providing an even higher confidence in the certification of the numerical code.
The article demonstrates the use of this tool on a real-size example, an
elementary function with correctly rounded output
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