From 44e9a4844f8408d8eb551f1e78dade0913ec8ef3 Mon Sep 17 00:00:00 2001
From: Apurv Kulkarni <apurv.kulkarni@tu-dresden.de>
Date: Mon, 15 Nov 2021 17:23:43 +0100
Subject: [PATCH] Corrected the config details. Config with 60GB doesn't work.
---
.../docs/software/big_data_frameworks_spark.md | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
diff --git a/doc.zih.tu-dresden.de/docs/software/big_data_frameworks_spark.md b/doc.zih.tu-dresden.de/docs/software/big_data_frameworks_spark.md
index 84f5935a1..5636a870a 100644
--- a/doc.zih.tu-dresden.de/docs/software/big_data_frameworks_spark.md
+++ b/doc.zih.tu-dresden.de/docs/software/big_data_frameworks_spark.md
@@ -36,11 +36,11 @@ Thus, Spark can be executed using different CPU architectures, e.g., Haswell and
Let us assume that two nodes should be used for the computation. Use a `srun` command similar to
the following to start an interactive session using the partition haswell. The following code
-snippet shows a job submission to haswell nodes with an allocation of two nodes with 60 GB main
+snippet shows a job submission to haswell nodes with an allocation of two nodes with 50 GB main
memory exclusively for one hour:
```console
-marie@login$ srun --partition=haswell --nodes=2 --mem=60g --exclusive --time=01:00:00 --pty bash -l
+marie@login$ srun --partition=haswell --nodes=2 --mem=50g --exclusive --time=01:00:00 --pty bash -l
```
Once you have the shell, load Spark using the command
@@ -129,7 +129,7 @@ example below:
#SBATCH --partition=haswell
#SBATCH --nodes=2
#SBATCH --exclusive
- #SBATCH --mem=60G
+ #SBATCH --mem=50G
#SBATCH --job-name="example-spark"
ml Spark/3.0.1-Hadoop-2.7-Java-1.8-Python-3.7.4-GCCcore-8.3.0
--
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