Performance Benchmarking

Using Legate timing tools

Use legate’s timing API to measure elapsed time, rather than standard Python timers. cuPyNumeric executes work asynchronously when possible, and a standard Python timer will only measure the time taken to launch the work, not the time spent in actual computation.

Make sure warm-up iterations, initialization, I/O, and other one-time computations are excluded while timing iterative computations.

Here is an example of how to measure elapsed time in milliseconds:

import cupynumeric as np
from legate.timing import time

init() # Initialization step

# Do few warm-up iterations
for i in range(n_warmup_iters):
    compute()

start = time()
for i in range(niters):
    compute()
end = time()

elapsed_millisecs = (end - start)/1000.0

dump_data() # I/O

Guidelines for performance benchmarks

Manual partitioning of data for use with message-passing from Python (say, using mpi4py package) is discouraged.

Ensure that the problem size is large enough to offset runtime overheads associated with tasks. A rule of thumb is that the problem size is large enough for a task granularity of about 1 millisecond (as of release 23.07).

For arrays that are small, or for arrays that operate on a subset of a larger array, it is recommended that they be merged with similar operations when possible. For example, in some applications using structured meshes, boundary conditions are set on a subset of data (at the boundaries only) which typically tends to be a sequence of very small operations. When possible, boundary conditions for different variables and different boundaries should be combined. In general, merging small operations might yield better results.