Hash tables

Close matches The close matches are the union of the upward chains from each member of IP(G) (not including supergraphs). In the most obvious implementation of Phase 2, a hash table is used to manage the breadth first search. It contains information about which nodes have been visited and which upward chains they are on. The desired union can be found simply by collecting elements of the hash table. 

There are several ways to facilitate an efihcient search in this list d. First consider a generic method that works whenever each receiver is a member of relatively few subsets 5 the values i, 2, are put in a hash table and in addition a perfect hash function h of the list is transmitted as well (see [15] for a recent survey of such functions). The length of the description of h can be relatively small compared to the length of the list i.e. it can be o m log w). The receiver u should check for all i such that u Si whether i is in the list by computing h i). In our case this would mean checking log TV values. 

Given a picture of a scene and a set of objects which can occur therein (called models in this context) both represented by points in 2D space, the goal is to recognize some of the models in the scene. In a preprocessing phase, a hash table is created from the set of models For each model, each pair of points define a so-called basis. Then, for each basis, every third point belonging to the model is expressed in coordinates relative to the basis. A tuple (model, basis) is stored in a hash table addressed by the relative coordinates of the third point. The reason for having several bases for a model instead of a single one is that it is unknown in advance, whether a part of the model is occluded in the scene. 

In the recognition phase the scene is analyzed as follows Every pair of points is considered as a basis. Once the basis is defined, all other points can be expressed by relative coordinates with respect to the basis resulting in a query for the hash table created before. The query votes for all matching tuples (model, basis) stored in the hash table. Finally, models with many votes are extracted, a transformation is calculated from the matching points, and the match is verified. 

The sequences of all reads /i,/2,... are concatenated together with a special character inserted at every read boundary. The resulting sequence is called the combined sequence. A hash table is constructed for the combined sequence, where for every word of length w, the table gives the positions of... 

If the Verlet method is used, then it turns out that the insertion of (3.18) into (3.19) results in a quartic polynomial that must be solved for each particle pair. Care must be taken in solving these quartics to ensure that roots are not missed. Using hash tables, neighbor lists and similar methods (like those used to compute non-bonded forces), the number of computations can be greatly reduced. Importantly, the calculations needed are all explicit and finite (no iteration is needed). 

The dictionary is large in size (two orders of magnitude larger than the cache). For best performance a hash table is used to realize the dictionary. Three different hash tables were used STL hash map, Google s sparsehash, and a custom bucket hash table [21]. All three are implemented in C-I--I- and are carefully optimized. The preexecution algorithm is the same for all three hash tables only the hash table invocation differs in the test cases. 

Figure 3.2 plots the number of operations (insert into the hash table or lookup in the hash table) executed per millisecond for the three different hash tables executed on two different hardwares, and Table 3.1 lists the final jumpahead distance and workahead size values estimated by the algorithm. The filled bars correspond to the desktop machine (D) plotted on the left vertical axes and the striped ones to the laptop (L) on the right axis (they are of different scales). 

All of the hash tables have different internal stractures, hence the different performance of the baselines. The adaptive preexecutiOTi model we presented achieves 2-30% speedups in all but one of the test cases (Google sparsehash executed on the laptop) on both machines. It is also notable that this speedup is also present for the third-party STL hash map and Google sparsehash. 

Juhasz S, Dudas A (2009) Adapting hash table design to real-life datasets. In Proceedings of the lADIS european conference on informatics 2009 (Part of the lADIS multiconference of computer science and information systems 2009). Algarve, Portugal, pp 3-10... 

We evaluated [16] the choice of suitable P2P technology for SCADA systems. Regarding the requirements of interconnected large-scale SCADA systems, structured P2P networks with Distributed Hash Tables (DHT) are appropriate for three reasons, i.e., (i) low routing latencies, (ii) good scalabihty, and (iii) data discovery guarantees in DHTs [16]. 

The initialization phase is also used to make a table of conflicts between the regvals. Each pair of regvals that cannot be stored in the same register due to timing conflicts is marked and the information is stored in a hash table for easy access during processing. 

To register, look up, or otherwise access a ring template, a canonical (i.e., unique and atom-numbering-independent) description of the RS is first obtained (Figure 54). The hash function reduces this to a short code, which acts as an address in the hash table. The hash table eventually yields the address of a ring data record where the ring is stored. 

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