docs: refine hyperfine options

Signed-off-by: Jérôme Benoit <jerome.benoit@sap.com>

diff --git a/benchmarks/versus-external-pools/hyperfine_benchmarks.sh b/benchmarks/versus-external-pools/hyperfine_benchmarks.sh
index 9979c573bdc3ec122799daebeaa2a9a696c1d2be..cf281a9735666d6df3e8d52b169d51b191f3e53b 100755 (executable)
--- a/benchmarks/versus-external-pools/hyperfine_benchmarks.sh
+++ b/benchmarks/versus-external-pools/hyperfine_benchmarks.sh
@@ -1,7 +1,6 @@
 #!/usr/bin/env bash
 
-hyperfine --export-markdown BENCH-100000.md --min-runs 10 \
-  --prepare 'sleep 15' \
+hyperfine --export-markdown BENCH-100000.md --min-runs 20 --prepare 'sleep 10' --warmup 10 \
   'node dynamic-poolifier.js' \
   'node fixed-poolifier.js' \
   'node dynamic-piscina.js' \

diff --git a/src/pools/selection-strategies/README.md b/src/pools/selection-strategies/README.md
index 78770b1b10040a84d2f951057c3ae4455be87ae1..95eee0079f6bbbb7d4bdb37c7b7cf665c076d1f4 100644 (file)
--- a/src/pools/selection-strategies/README.md
+++ b/src/pools/selection-strategies/README.md
@@ -7,7 +7,7 @@ All duration or timestamp are expressed in milliseconds.
 ### Fair share
 
 Its goal is to distribute the load evenly across all workers. To achieve this, the strategy keeps track of the average task execution time for each worker and assigns the next task to the worker with the lowest task end prediction time: `task_end_prediction = max(current_time, task_end_prediction) + average_task_execution_time`.  
-By default, the strategy uses the average task execution time for each worker but it can be configured to use the task event loop utilization (ELU) active time instead.
+By default, the strategy uses the average task execution time for each worker but it can be configured to use the average task event loop utilization (ELU) active time instead.
 
 ### Weighted round robin