diff --git a/cscs-checks/apps/spark/spark_check.py b/cscs-checks/apps/spark/spark_check.py
index 3a89e5483a..97e716f3c2 100644
--- a/cscs-checks/apps/spark/spark_check.py
+++ b/cscs-checks/apps/spark/spark_check.py
@@ -3,47 +3,29 @@
 #
 # SPDX-License-Identifier: BSD-3-Clause
 
-import math
-
 import reframe as rfm
-import reframe.utility.sanity as sn
-from reframe.core.backends import getlauncher
 
+from reframe.core.backends import getlauncher
+from hpctestlib.apps.spark.compute_pi import ComputePi
 
 @rfm.simple_test
-class SparkCheck(rfm.RunOnlyRegressionTest):
-    variant = parameter(['spark', 'pyspark'])
-
-    def __init__(self):
-        self.descr = f'Simple calculation of pi with {self.variant}'
-        self.valid_systems = ['daint:gpu', 'daint:mc',
-                              'dom:gpu', 'dom:mc']
-        self.valid_prog_environs = ['builtin']
-        self.modules = ['Spark']
-        self.prerun_cmds = ['start-all.sh']
-        self.postrun_cmds = ['stop-all.sh']
-        self.num_tasks = 3
-        self.num_tasks_per_node = 1
-        pi_value = sn.extractsingle(r'Pi is roughly\s+(?P<pi>\S+)',
-                                    self.stdout, 'pi', float)
-        self.sanity_patterns = sn.assert_lt(sn.abs(pi_value - math.pi), 0.01)
-        self.maintainers = ['TM', 'RS']
-        self.tags = {'production'}
+class SparkCheck(ComputePi):
+    valid_systems = ['daint:gpu', 'daint:mc', 'dom:gpu', 'dom:mc']
+    valid_prog_environs = ['builtin']
+    modules = ['Spark']
+    num_tasks = 3
+    num_tasks_per_node = 1
+    maintainers = ['TM', 'RS']
+    tags = {'production'}
 
     @run_before('run')
     def prepare_run(self):
-        if self.current_partition.fullname in ['daint:gpu', 'dom:gpu']:
-            num_workers = 12
-            exec_cores = 3
-        else:
-            num_workers = 36
-            exec_cores = 9
-
+        num_workers = self.current_partition.processor.num_cores
+        exec_cores = num_workers // 4
         self.variables = {
             'SPARK_WORKER_CORES': str(num_workers),
             'SPARK_LOCAL_DIRS': '"/tmp"',
         }
-        self.executable = 'spark-submit'
         self.executable_opts = [
             f'--conf spark.default.parallelism={num_workers}',
             f'--conf spark.executor.cores={exec_cores}',
@@ -55,9 +37,11 @@ def prepare_run(self):
                 '--class org.apache.spark.examples.SparkPi',
                 '$EBROOTSPARK/examples/jars/spark-examples*.jar 10000'
             ]
-        else:
-            self.executable_opts.append('spark_pi.py')
+        elif self.variant == 'pyspark':
+            self.executable_opts += ['spark_pi.py']
 
+    @run_before('run')
+    def set_job_launcher(self):
         # The job launcher has to be changed since the `spark-submit`
         # script is not used with srun.
         self.job.launcher = getlauncher('local')()
diff --git a/hpctestlib/apps/spark/compute_pi/__init__.py b/hpctestlib/apps/spark/compute_pi/__init__.py
new file mode 100644
index 0000000000..323bee711d
--- /dev/null
+++ b/hpctestlib/apps/spark/compute_pi/__init__.py
@@ -0,0 +1,48 @@
+# Copyright 2016-2021 Swiss National Supercomputing Centre (CSCS/ETH Zurich)
+# ReFrame Project Developers. See the top-level LICENSE file for details.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+import math
+
+import reframe as rfm
+import reframe.utility.sanity as sn
+
+
+class ComputePi(rfm.RunOnlyRegressionTest, pin_prefix=True):
+    '''Base class for the Spark Test.
+
+    Apache Spark is a unified analytics engine for large-scale data
+    processing. It provides high-level APIs in Java, Scala, Python
+    and R, and an optimized engine that supports general execution
+    graphs. It also supports a rich set of higher-level tools including
+    Spark SQL for SQL and structured data processing, MLlib for machine
+    learning, GraphX for graph processing, and Structured Streaming for
+    incremental computation and stream processing (see spark.apache.org).
+
+    The present abstract run-only class checks the Spark perfomance.
+    To do this, it is necessary to define the tolerance of acceptable
+    deviation. The tolerance is used to check if the task executed correctly,
+    comparing the value of pi calculated to the one obtained from the math
+    library. The default assumption is that Spark is already installed on the
+    system under test.
+    '''
+
+    variant = parameter(['spark', 'pyspark'])
+    tolerance = variable(float, value=0.01)
+    prerun_cmds = ['start-all.sh']
+    postrun_cmds = ['stop-all.sh']
+    executable = 'spark-submit'
+    executable_opts = required
+
+    @run_after('init')
+    def set_description(self):
+        self.mydescr = f'Simple calculation of pi with {self.variant}'
+
+    @sanity_function
+    def assert_pi_readout(self):
+        '''Assert that the obtained pi value meets the specified tolerances.'''
+
+        pi_value = sn.extractsingle(r'Pi is roughly\s+(?P<pi>\S+)',
+                                    self.stdout, 'pi', float)
+        return sn.assert_lt(sn.abs(pi_value - math.pi), self.tolerance)
diff --git a/cscs-checks/apps/spark/src/spark_pi.py b/hpctestlib/apps/spark/compute_pi/src/spark_pi.py
similarity index 100%
rename from cscs-checks/apps/spark/src/spark_pi.py
rename to hpctestlib/apps/spark/compute_pi/src/spark_pi.py