Graphical processor-based graph computing node vector load balancing method and device
A graphics processor and load balancing technology, applied in computing, multi-programming devices, electrical digital data processing, etc., can solve the problems of increased atomic operation overhead, increased computing overhead, unbalanced topology structure distribution, etc., to simplify programming, Simple hardware, the effect of reducing overhead
Active Publication Date: 2022-05-24
NAT UNIV OF DEFENSE TECH
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Problems solved by technology
[0003] However, due to the unbalanced distribution of graph topology, the load balancing problem has always been a key issue that plagues the realization of efficient parallel graph computing analysis.
However, on the one hand, the load balancing strategy will increase additional computing overhead, and on the other hand, since the active node vector is shared by multiple threads, the overhead of atomic operations will also increase significantly, thereby reducing the performance of parallel graph computing
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[0027] like figure 1 As shown, the vector load balancing method for graph computing nodes based on the graphics processor in this embodiment includes a scheduling step for the control processor of the graphics processor to perform load balancing, and the scheduling step includes:
[0028] 1) The control processor of the graphics processor periodically finds the longest and shortest node vector lengths among all SM cores and calculates the difference;
[0029] 2) The control processor of the graphics processor determines whether the calculated difference exceeds the preset threshold, and if it has not exceeded the preset threshold, it will end and exit; otherwise, it will jump to the next step;
[0030] 3) The control processor of the graphics processor selects two SM cores that need to be load balanced, sets the states of the two SM cores to the state of waiting for donation load and the state of receiving load donation respectively, and calculates the number of nodes that nee...
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The invention discloses a graph computing node vector load balancing method and system based on a graphics processor. The method of the invention includes regularly finding the longest and shortest node vector lengths among all SM cores and calculating the difference. The difference exceeds the preset If the threshold is set, two SM cores are selected, and they are respectively set to the state of waiting for donated load and receiving load donation; a load balancing request is sent to the SM core whose status is receiving load donation, and the SM core successfully applies for a storage space address in the global storage After notification, the SM core whose state is waiting for the donated load starts to transfer the data of the node vector, and the transferred node vector data will be written into the storage space address requested by the load receiver. The invention can efficiently realize the vector load balance of graph computing nodes among the cores of graphics processors and stream processors, has the advantages of simple hardware structure and transparent programming for programmers, and can complete all loads without modifying the original program balance task.
Description
technical field [0001] The invention relates to a load balancing technology of a graphics processor, in particular to a method and device for balancing the vector load of a graph computing node based on a graphics processor. Background technique [0002] With the rapid development of application technologies such as the Internet of Things (IoT), mobile devices, and social networks, the amount of data generated by these applications is increasing rapidly at a rate of 34% each year. The unstructured data represented by graphs, Its average annual growth rate has reached 65%. Faced with such a huge amount of data, how to complete the data analysis in an effective time and mine valuable information from it is a challenge faced by data analysts. With the increasing maturity of GPU technology, it has become a general-purpose large-scale parallel computing accelerator. Compared with general-purpose microprocessors, the architectural advantages of GPUs are: (1) a large number of si...
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Login to View More IPC IPC(8): G06F9/50
CPCG06F9/505G06F9/5083
Inventor 黄立波郭辉郑重郭维雷国庆王俊辉隋兵才孙彩霞王永文倪晓强
Owner NAT UNIV OF DEFENSE TECH