iter: The current iteration count. The letter "r" is appended for iterations in restoration phase. objective: The unscaled objective value. During the restoration phase, this value remains the unscaled objectiv
This parameter indicates how often (at some iteration count) that we should move onto the next “step” of training. This value is a positive integer. stepvalue This parameter indicates one of potentially many iteration counts that we should move onto the next “step” of training. This value...
Generic_F1_310M • t8 original – t8 with 8 GPUs • t32 remap – t32 with 8 GPUs remapped • I/O time significantly reduced • Setup time moderately increased • Iteration time lightly increased • Overall, 25% performance gain Generic_F1_310M CPU/GPU Remapping 2000 1800 1600...
void Solver<Dtype>::Test(const int test_net_id) { LOG(INFO) << "Iteration " << iter_ << ", Testing net (#" << test_net_id << ")"; // We need to set phase to test before running. Caffe::set_phase(Caffe::TEST); CHECK_NOTNULL(test_nets_[test_net_id].get())-> ...
(iteration_parameters.number_of_base_nodes), iteration_parameters_(std::move(iteration_parameters)), optimal_paths_enabled_(false), active_paths_(number_of_nexts_), alternative_index_(next_vars.size(), -1) { DCHECK_GT(iteration_parameters_.number_of_base_nodes, 0); if...
For this case, the vast majority of the iteration phase is spent in the linear solver, which makes it ideal to offload the computationally intensive linear system solving to GPUs. Measuring the iteration time of CODA provides a very close estimation of the performance and scalability of Spliss ...
However, because the GRAPE algorithm itself is highly non-trivial, the compilation latency for each iteration is of critical concern. Two of the GRAPE-based techniques are briefly described below; see Gokhale et al.43 for a detailed discussion. The GRAPE compilation technique employs an optimal ...
However, because the GRAPE algorithm itself is highly non-trivial, the compilation latency for each iteration is of critical concern. Two of the GRAPE-based techniques are briefly described below; see Gokhale et al.43 for a detailed discussion. The GRAPE compilation technique employs an optimal ...
at each iteration i, the mobility of the p-th point μp is inversely proportional to its diameter Dp, which physically means that small and light points have high mobility and large points have low mobility due to the effect of inertia, where 〈D〉 is the mean diameter of the material ...
iter: The current iteration count. The letter "r" is appended for iterations in restoration phase. objective: The unscaled objective value. During the restoration phase, this value remains the unscaled objective value for the original problem. ...