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Tarjan's strongly connected components algorithm is an algorithm in graph theory for finding the strongly connected components (SCCs) of a directed graph. It runs in linear time , matching the time bound for alternative methods including Kosaraju's algorithm and the path-based strong component algorithm .
In order to fully utilize the bandwidth of different types of memory such as caches and memory banks, few compilers or CPU architectures ensure perfectly strong ordering. [1] [5] Among the commonly used architectures, x86-64 processors have the strongest memory order, but may still defer memory store instructions until after memory load ...
The memory model stipulates that changes to the values of shared variables only need to be made visible to other threads when such a synchronization barrier is reached. Moreover, the entire notion of a race condition is defined over the order of operations with respect to these memory barriers. [1]
In order to reduce the prediction latency, Jimenez proposed in 2003 the fast-path neural predictor, where the perceptron predictor chooses its weights according to the current branch's path, rather than according to the branch's PC. Many other researchers developed this concept (A. Seznec, M. Monchiero, D. Tarjan & K. Skadron, V. Desmet, Akkary ...
Now an order 1 block is available, so it is allocated to D. Program B releases its memory, freeing one order 1 block. Program D releases its memory. One order 1 block is freed. Since the buddy block of the newly freed block is also free, the two are merged into one order 2 block. Program A releases its memory, freeing one order 0 block.
The following multi-threaded program, running on a multi-core processor gives an example of how such out-of-order execution can affect program behavior: Initially, memory locations x and f both hold the value 0. The software thread running on processor #1 loops while the value of f is zero, then it prints the value of x.
Graphs of functions commonly used in the analysis of algorithms, showing the number of operations N as the result of input size n for each function. In theoretical computer science, the time complexity is the computational complexity that describes the amount of computer time it takes to run an algorithm.
The Z-ordering can be used to efficiently build a quadtree (2D) or octree (3D) for a set of points. [4] [5] The basic idea is to sort the input set according to Z-order.Once sorted, the points can either be stored in a binary search tree and used directly, which is called a linear quadtree, [6] or they can be used to build a pointer based quadtree.