Custom HPC software & tools from Wirawan. Primarily tailored toward ODU HPC sytems.
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#!/usr/bin/env python
#
# 20160920
# Wirawan Purwanto
#
def est_hpl_timing(N, nprocs, proc_gflops, eff=0.8):
"""Estimates the time it takes to do HPL calculation on
an (N x N) problem, given `nprocs` processor cores which has
`proc_gflops` GFLOPS.
"""
# Number of floating point operations
# From HPL code, estimated to be
# 2/3 N^3 - 1/2 N^2 flops for LU factorization + 2 N^2 flops for solve.
assert N > 0
assert nprocs >= 1
assert proc_gflops > 0
assert 0.0 < eff <= 1.0
N = float(N)
num_gflop = (2 * N**3 / 3 - 0.5 * N**2 + 2 * N**2) * 1e-9
tot_proc_gflops = nprocs * proc_gflops
est = num_gflop / tot_proc_gflops / eff
proc_mem_gb = (N**2 * 1e-9 * 8) / nprocs
#if verbose >= 1:
return (est, num_gflop, tot_proc_gflops, proc_mem_gb)
def est_hpl_timing2(proc_mem_gb, nprocs, proc_gflops, eff=0.8):
"""Estimates the time it takes to do HPL calculation on
a problem specified by `proc_mem_gb` RAM per core (in GB),
`nprocs` processor cores, each having `proc_gflops` GFLOPS.
We assume the matrix is evenly distributed across processors in
a square fashion (i.e. P == Q for tile definition).
"""
from math import sqrt
N1 = sqrt(proc_mem_gb * 1e9 / 8)
N = float(int(N1 * sqrt(nprocs)))
(est, num_gflop, tot_proc_flops, proc_mem_gb0) = \
est_hpl_timing(N, nprocs, proc_gflops, eff)
return est, num_gflop, tot_proc_flops, N
def Test_64core_memscale(proc_mem_gb=[0.15, 0.25, 0.50, 0.780125, 1.25, 2.00, 3.00],
proc_gflops=17.6, eff=0.8):
"""[20160920]
Test: keep at 64 cores, scale up memory and see how much time it takes."""
nproc = 64
from wpylib.text_tools import str_fmt_heading
cols = ("N", "mem/proc", "nproc", "time", "numops_gf", "proc_gf")
fmt = "%8d %8.3f %5d %6.0f %10.2f %10.2f"
hfmt = str_fmt_heading(fmt)
print(hfmt % cols)
for pm1 in proc_mem_gb:
(est_t, num_gflop, tot_proc_gflops, N) = \
est_hpl_timing2(pm1, nproc, proc_gflops, eff)
print(fmt % (N, pm1, nproc, est_t, num_gflop, tot_proc_gflops))