... E5-2680 v4 @ 2.40 GHz 8 x Intel Xeon CPU E5-2680 v4 @ 2.40 GHz Memory 8 GB 8 GB Disk space 4 GB 20 GB Section 3.3 presents a description of obtained log file messages and monitoring metrics, which then refer to as features of our ...
... e5-2680 V4 chip. Each node had 28 cores and was configured with 132G memory. When using single process and single thread, the original model took 351 s to finish building, while our accelerated model took only 62 s to finish building ...
... E5-2680 v4, and 66 GB memory. (B). The computational time (in s) for per iteration of FMixFN according to the number of individuals increasing in the reference group. Computing performance tests were performed on a Red Hat Enterprise ...
... ( E5-2680 - V4 ) fourteen - core processors , 128 GB of RAM , 120 GB of local HDD , and two NVIDIA Tesla K80 GPUs . As shown in figure 10.9 , for Caffe OnSpark , Accuracy ( % ) 40 30 20 10 286 80 Deep Learning over Big Data 189.
... E5-2680 v4 2.4GHz CPU, which has a 35 MB Intel SmartCache. The machine has 256 gigabytes of memory at a speed of 2400MT/s. The code was compiled with g++ using the -Ofast optimization flag. References 1. Bender, M.A., Farach-Colton, M ...
... E5-2680 v4 for a maximum execution time of 30 min each . For each algorithms , instances of both benchmarks were run a total of 10 times . Fitness function coefficient ( see Sect . 1 ) used are : Aveh 1000 , anl = 500 , adist = 1 , apen ...
... E5-2680 V4 processors with 2.4 GHz clock speed . Therefore , such CFD modeling is not eligible for optimization studies where a vast number of simulations are performed . Throughflow models are reduced - order models for turbomachinery ...
... E5-2680 v4@2.40GHz . The GPU parameters are NVIDIA GeForce RTX 3080 , Cuda component parameters are cuda11.3.1 and cudnn 9.1 , and Python 3.8.10 and Pytorch 1.10.0 are used . To evaluate the performance of the network model , Precision ...
... E5-2680 v4 @ 2.40 GHz and 64GB memory. We adopt Gurobi [6] as our LP solver. We set the lower bound ε to 1e − 5. Empirically, we set the lower bound tolerance to 0.5. We mainly adopt two popular datasets MNIST and CIFAR-10 in the ...