More dependent types for distributed arrays
نویسندگان
چکیده
منابع مشابه
More dependent types for distributed arrays
Locality-aware algorithms over distributed arrays can be very difficult to write. Yet such algorithms are becoming more and more important as desktop machines boast more and more processors. This paper shows how a dependently-typed programming language can aid in the development of these algorithms and statically ensure that every well-typed program will only ever access local data. Such static...
متن کاملDependent Types for Distributed Arrays
Locality-aware algorithms over distributed arrays can be very difficult to write. Yet such algorithms are becoming more and more important as desktop machines boast more and more processors. We show how a dependently-typed programming language can help develop such algorithms by hosting a domainspecific embedded language that ensures every well-typed program will only ever access local data. Su...
متن کاملSafe Arrays via Regions and Dependent Types
Arrays over regions of points were introduced in ZPL in the late 1990s and later adopted in Titanium and X10 as a means of simplifying the programming of high-performance software. A region is a set of points, rather than an interval or a product of intervals, and enables the programmer to write a loop that iterates over a region. While convenient, regions do not eliminate the risk of array bou...
متن کاملDescriptor-Free Representation of Arrays with Dependent Types
Besides element type and values, a multidimensional array is characterized by the number of axes (rank) and their respective lengths (shape vector). Both properties are essential to do bound checking and to compute linear offsets into heap memory at run time. In order to have an array’s rank and shape available at any time during program execution both are typically kept in an array descriptor ...
متن کاملMARCINKIEWICZ-TYPE STRONG LAW OF LARGE NUMBERS FOR DOUBLE ARRAYS OF NEGATIVELY DEPENDENT RANDOM VARIABLES
In the following work we present a proof for the strong law of large numbers for pairwise negatively dependent random variables which relaxes the usual assumption of pairwise independence. Let be a double sequence of pairwise negatively dependent random variables. If for all non-negative real numbers t and , for 1 < p < 2, then we prove that (1). In addition, it also converges to 0 in ....
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Higher-Order and Symbolic Computation
سال: 2010
ISSN: 1388-3690,1573-0557
DOI: 10.1007/s10990-011-9075-y