diff --git a/orangecontrib/spectroscopy/widgets/owspectra.py b/orangecontrib/spectroscopy/widgets/owspectra.py
index 06a877259..4558d0bc0 100644
--- a/orangecontrib/spectroscopy/widgets/owspectra.py
+++ b/orangecontrib/spectroscopy/widgets/owspectra.py
@@ -2,12 +2,16 @@
 import sys
 from collections import defaultdict
 import random
+import time
 import warnings
 from xml.sax.saxutils import escape
 
 try:
     import dask
     import dask.array as da
+    import dask.distributed
+    dask_client = dask.distributed.Client(processes=False, n_workers=2,
+                                          threads_per_worker=4, dashboard_address=None)
 except ImportError:
     dask = None
 
@@ -304,6 +308,8 @@ def compute_averages(data: Orange.data.Table, color_var, subset_indices,
 
         def progress_interrupt(i: float):
             if state.is_interruption_requested():
+                if future:
+                    future.cancel()
                 raise InterruptException
 
         def _split_by_color_value(data, color_var):
@@ -324,7 +330,12 @@ def _split_by_color_value(data, color_var):
 
         results = []
 
+        future = None
+
+        is_dask = dask and isinstance(data.X, dask.array.Array)
+
         dsplit = _split_by_color_value(data, color_var)
+        compute_dask = []
         for colorv, indices in dsplit.items():
             for part in [None, "subset", "selection"]:
                 progress_interrupt(0)
@@ -335,11 +346,11 @@ def _split_by_color_value(data, color_var):
                 elif part == "subset":
                     part_selection = indices & subset_indices
                 if np.any(part_selection):
-                    if dask and isinstance(data.X, da.Array):
+                    if is_dask:
                         subset = data.X[part_selection]
-                        std = da.nanstd(subset, axis=0)
-                        mean = da.nanmean(subset, axis=0)
-                        std, mean = dask.compute(std, mean)
+                        compute_dask.extend([da.nanstd(subset, axis=0),
+                                             da.nanmean(subset, axis=0)])
+                        std, mean = None, None
                     else:
                         std = apply_columns_numpy(data.X,
                                                   lambda x: bottleneck.nanstd(x, axis=0),
@@ -349,7 +360,19 @@ def _split_by_color_value(data, color_var):
                                                    lambda x: bottleneck.nanmean(x, axis=0),
                                                    part_selection,
                                                    callback=progress_interrupt)
-                    results.append((colorv, part, mean, std, part_selection))
+                    results.append([colorv, part, mean, std, part_selection])
+
+        if is_dask:
+            future = dask_client.compute(dask.array.vstack(compute_dask))
+            while not future.done():
+                progress_interrupt(0)
+                time.sleep(0.1)
+            if future.cancelled():
+                return
+            computed = future.result()
+            for i, lr in enumerate(results):
+                lr[2] = computed[i*2]
+                lr[3] = computed[i*2+1]
 
         progress_interrupt(0)
         return results
@@ -412,13 +435,28 @@ def show(self):
 
     @staticmethod
     def compute_curves(x, ys, sampled_indices, state: TaskState):
+        is_dask = dask and isinstance(ys, dask.array.Array)
+
         def progress_interrupt(i: float):
             if state.is_interruption_requested():
+                if future:
+                    future.cancel()
                 raise InterruptException
 
+        future = None
+
         progress_interrupt(0)
-        ys = np.asarray(ys[sampled_indices])
+        ys = ys[sampled_indices]
+        if is_dask:
+            future = dask_client.compute(ys)
+            while not future.done():
+                progress_interrupt(0)
+                time.sleep(0.1)
+            if future.cancelled():
+                return
+            ys = future.result()
         ys[np.isinf(ys)] = np.nan  # remove infs that could ruin display
+
         progress_interrupt(0)
         return x, ys, sampled_indices