988 research outputs found
Space-Efficient DFS and Applications: Simpler, Leaner, Faster
The problem of space-efficient depth-first search (DFS) is reconsidered. A
particularly simple and fast algorithm is presented that, on a directed or
undirected input graph with vertices and edges, carries out a
DFS in time with bits of working memory, where is the
(total) degree of , for each , and . A slightly more complicated variant of the algorithm works in the same
time with at most bits. It is also shown that a DFS can
be carried out in a graph with vertices and edges in
time with bits or in time with either
bits or, for arbitrary integer , bits. These
results among them subsume or improve most earlier results on space-efficient
DFS. Some of the new time and space bounds are shown to extend to applications
of DFS such as the computation of cut vertices, bridges, biconnected components
and 2-edge-connected components in undirected graphs
Answering Spatial Multiple-Set Intersection Queries Using 2-3 Cuckoo Hash-Filters
We show how to answer spatial multiple-set intersection queries in O(n(log
w)/w + kt) expected time, where n is the total size of the t sets involved in
the query, w is the number of bits in a memory word, k is the output size, and
c is any fixed constant. This improves the asymptotic performance over previous
solutions and is based on an interesting data structure, known as 2-3 cuckoo
hash-filters. Our results apply in the word-RAM model (or practical RAM model),
which allows for constant-time bit-parallel operations, such as bitwise AND,
OR, NOT, and MSB (most-significant 1-bit), as exist in modern CPUs and GPUs.
Our solutions apply to any multiple-set intersection queries in spatial data
sets that can be reduced to one-dimensional range queries, such as spatial join
queries for one-dimensional points or sets of points stored along space-filling
curves, which are used in GIS applications.Comment: Full version of paper from 2017 ACM SIGSPATIAL International
Conference on Advances in Geographic Information System
Fast deterministic processor allocation
Interval allocation has been suggested as a possible formalization for the PRAM of the (vaguely defined) processor allocation problem, which is of fundamental importance in parallel computing. The interval allocation problem is, given nonnegative integers , to allocate nonoverlapping subarrays of sizes from within a base array of cells. We show that interval allocation problems of size can be solved in time with optimal speedup on a deterministic CRCW PRAM. In addition to a general solution to the processor allocation problem, this implies an improved deterministic algorithm for the problem of approximate summation. For both interval allocation and approximate summation, the fastest previous deterministic algorithms have running times of . We also describe an application to the problem of computing the connected components of an undirected graph
On a compaction theorem of ragde
Ragde demonstrated that in constant time a PRAM with processors can move at most items, stored in distinct cells of an array of size , to distinct cells in an array of size at most . We show that the exponent of 4 in the preceding sentence can be replaced by any constant greater than~2
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