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Train	application_name	system_name	processors	num_mpi	num_omppn	input	frequency	executable_name	scenario	frequency	papi_tot_cyc	papi_tot_ins	papi_l1_tcm	papi_l2_tcm	papi_fml_ins	papi_br_tkn	papi_l2_ldm	papi_l2_stm	papi_fdv_ins	papi_br_cn	papi_tot_iis	papi_ca_shr	papi_ca_cln	papi_ca_itv	papi_br_ntk	papi_br_msp	papi_fp_ins	papi_res_stl	papi_l1_ldm	papi_l1_stm	papi_l2_tcw	papi_l2_dca	papi_l2_dcr	papi_l2_dcw	papi_l1_icm	papi_br_ins	papi_l1_dch	papi_l1_dca	papi_l1_dcm	papi_l1_tca	papi_l1_ica	papi_fp_ops	papi_l2_ica	papi_tlb_dm	papi_vec_ins	papi_l2_dcm	papi_l2_icm	papi_l2_tch	papi_l2_tca	papi_ld_ins	papi_sr_ins	papi_tlb_im	papi_hw_int	papi_l3_tcm	nruntime	power_sys	power_cpu	power_mem	num_mpi	num_omppn	scaling_factor
	Horovod CANDLE NT3 Benchmark	ANL Theta	48	48	64	learning rate=0.001, batch size=70.0, epochs=48.0	1.301	nt3-hybrid-opt.py	empirical	1.301	1	0.5988338265437401	0.0178809862848635	0.0020485194743175	0.0	0.0303881214553196	0.0010233867736137	0.0	0.0	0.1118295234367062	0.0	0.0	0.0	0.0	0.0811568433075185	0.0070325649666903	0.0	0.1729514662163626	0.005856923363513	0.0	0.0	0.0	0.0	0.0	0.0120828904326972	0.1435023638573317	0.0	0.2269212565297892	0.005856923363513	0.0	0.4243131524948982	0.0	0.0	0.0047045530830789	0.0	0.0	0.0	0.0173665570690781	0.0194150765433955	0.1573315821371322	0.063067546131056	0.0	0.0	0.0	4.93508573584e-09	145.377736	92.479675	11.290807	48	64	161959008573.121462255321481605