ALI Performance Tests on Weaver

Introduction

Currently testing the Greenland Ice Sheet (GIS) in Albany Land Ice (ALI) using Nvidia Tesla V100 GPUs on weaver. Note: Waterman was moved to a different network and renamed weaver. The last waterman test was executed on 6/16/2020.

Architectures:

Name	Weaver (P9/V100)
CPU	Dual-socket IBM POWER9
GPU	Nvidia Tesla V100
Cores/Node	40
Threads/Core	8
GPUs/Node	4
Memory/Node	319 GB
Interconnect	Mellanox EDR IB (100 GB/s)
Compiler	gcc 7.2.0
GPU Compiler	cuda 10.1.105
MPI	openmpi 4.0.1

Cases:

Case Name	Number of Processes (np)	Description
green-1-10km_ent_fea_mem_tet	8	Unstructured 1-10km GIS, Enthalpy problem, finite element assembly only, memoization, tetrahedron
green-1-7km_fea_1ws	8	Unstructured 1-7km GIS, finite element assembly only, single workset
green-1-7km_fea_mem	8	Unstructured 1-7km GIS, finite element assembly only, memoization
green-1-7km_muk_ls_mem	8	Unstructured 1-7km GIS, MueLu w/ kokkos and line smoothing, memoization
green-3-20km_vel_fea_mem_tet	8	Unstructured 3-20km GIS, Velocity problem, finite element assembly only, memoization, tetrahedron
green-3-20km_vel_fea_mem_wdg	8	Unstructured 3-20km GIS, Velocity problem, finite element assembly only, memoization, wedge
green-3-20km_ent_fea_mem_tet	8	Unstructured 3-20km GIS, Enthalpy problem, finite element assembly only, memoization, tetrahedron
green-3-20km_ent_fea_mem_wdg	8	Unstructured 3-20km GIS, Enthalpy problem, finite element assembly only, memoization, wedge

Timers:

Timer Name	Level	Description
Albany Total Time	0	Total wall-clock time of simulation
Albany: Setup Time	1	Preprocess
Albany: Total Fill Time	1	Finite element assembly
Albany Fill: Residual	2	Residual assembly
Albany Residual Fill: Evaluate	3	Compute the residual, local/global assembly
Albany Residual Fill: Export	3	Update global residual across MPI ranks
Albany Fill: Jacobian	2	Jacobian assembly
Albany Jacobian Fill: Evaluate	3	Compute the Jacobian, local/global assembly
Albany Jacobian Fill: Export	3	Update global Jacobian across MPI ranks
NOX Total Preconditioner Construction	1	Construct Preconditioner
NOX Total Linear Solve	1	Linear Solve

Specifications

Test cases:
  green-1-10km_ent_fea_1ws_tet_np8
  green-1-10km_ent_fea_mem_tet_np8
  green-1-10km_ent_muk_tet_np8
  green-1-7km_fea_1ws_np8
  green-1-7km_fea_mem_np8
  green-1-7km_muk_ls_mem_np8
  green-3-20km_vel_fea_mem_tet_np8
  green-3-20km_vel_fea_mem_wdg_np8
  green-3-20km_vel_muk_wdg_np8
  green-3-20km_ent_fea_mem_tet_np8
  green-3-20km_ent_fea_mem_wdg_np8
  green-3-20km_ent_muk_wdg_np8
Metrics:
  Total Time
  Setup Time
  Total Fill Time
  Residual Fill
  Residual Fill Evaluate
  Residual Fill Export
  Jacobian Fill
  Jacobian Fill Evaluate
  Jacobian Fill Export
  NOX Total Preconditioner Construction
  NOX Total Linear Solve
  max host memory
  max kokkos memory
Metadata:
  Albany cxx compiler
  Albany git commit id
  Trilinos git commit id
Model threshold: 0.005000

Performance Timelines

Events in the most recent 10 days:
02/02/2022:
    green-1-10km_ent_fea_mem_tet_np8: NOX Total Preconditioner Construction
    green-3-20km_vel_muk_wdg_np8: Jacobian Fill

Plot of wall-clock times or memory for nightly runs

Changepoints are estimated using a generalized likelihood ratio method on each timer, and then merged over all timers for a given test case.

• Blue markers: recorded wall-clock time or memory
• Solid red line: average wall-clock time or memory between changepoints
• Dotted red lines: average wall-clock time or memory $\pm$ two standard deviations

Plot window controls

• Test case and timer can be selected from the drop-down menus (* denotes recent changes detected)
• Hovering over data points shows various metadata.
  • The mean with upper and lower bounds of a 99% confidence interval.
  • The relative performance (speedup,slowdown) between sets identified by changepoints with a 99% confidence interval for the ratio (difference between log mean of the two sets).
  • Note: The confidence interval is based on a t-statistic, so for very small amounts of data (<5), the interval may be very large. NaNs are converted to zero.
• Clicking on the legend will show/hide individual plot elements
• Click and drag to zoom in; double click to reset zoom

Pollak, Moshe; Siegmund, D. Sequential Detection of a Change in a Normal Mean when the Initial Value is Unknown. Ann. Statist. 19 (1991), no. 1, 394--416. doi:10.1214/aos/1176347990. https://projecteuclid.org/euclid.aos/1176347990

Siegmund, D.; Venkatraman, E. S. Using the Generalized Likelihood Ratio Statistic for Sequential Detection of a Change-Point. Ann. Statist. 23 (1995), no. 1, 255--271. doi:10.1214/aos/1176324466. https://projecteuclid.org/euclid.aos/1176324466

Hawkins, D. M., & Zamba, K. D. (2005). Statistical Process Control for Shifts in Mean or Variance using a Change Point Formulation. Technometrics, 47, 164-173.

Hawkins DM, Qiu P, Kang CW. The changepoint model for statistical process control. Journal of Quality Technology. 2003 Oct 1;35(4):355-366.