diff --git a/emcee/utils.py b/emcee/utils.py
index c5685d30..77f06aab 100644
--- a/emcee/utils.py
+++ b/emcee/utils.py
@@ -163,20 +163,29 @@ def map(self, function, tasks):
             F = _function_wrapper(function)
 
             # Tell all the workers what function to use.
+            requests = []
             for i in range(self.size):
-                self.comm.isend(F, dest=i + 1)
+                r = self.comm.isend(F, dest=i + 1)
+                requests.append(r)
 
-            # Send all the tasks off. Do not wait for them to be received,
-            # just continue.
+            # Wait until all of the workers have responded. See:
+            #       https://gist.github.com/4176241
+            MPI.Request.waitall(requests)
+
+            # Send all the tasks off and wait for them to be received.
+            # Again, see the bug in the above gist.
+            requests = []
             for i, task in enumerate(tasks):
                 worker = i % self.size + 1
                 if self.debug:
                     print(u"Sent task {0} to worker {1} with tag {2}."
                             .format(task, worker, i))
-                self.comm.isend(task, dest=worker, tag=i)
-            results = []
+                r = self.comm.isend(task, dest=worker, tag=i)
+                requests.append(r)
+            MPI.Request.waitall(requests)
 
             # Now wait for the answers.
+            results = []
             for i in range(ntask):
                 worker = i % self.size + 1
                 if self.debug:
diff --git a/examples/mpi.py b/examples/mpi.py
index 7c322c54..45aaf49a 100644
--- a/examples/mpi.py
+++ b/examples/mpi.py
@@ -16,9 +16,11 @@
 def lnprob(x):
     return -0.5 * np.sum(x ** 2)
 
-ndim = 10
-nwalkers = 200
+ndim = 50
+nwalkers = 250
 p0 = [np.random.rand(ndim) for i in xrange(nwalkers)]
+
+# Initialize the MPI-based pool used for parallelization.
 pool = MPIPool()
 
 if not pool.is_master():
@@ -30,7 +32,7 @@ def lnprob(x):
 sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob, pool=pool)
 
 # Run 100 steps as a burn-in.
-pos, prob, state = sampler.run_mcmc(p0, 1)
+pos, prob, state = sampler.run_mcmc(p0, 100)
 
 # Reset the chain to remove the burn-in samples.
 sampler.reset()
@@ -43,16 +45,6 @@ def lnprob(x):
 pool.close()
 
 # Print out the mean acceptance fraction. In general, acceptance_fraction
-# has an entry for each walker so, in this case, it is a 100-dimensional
+# has an entry for each walker so, in this case, it is a 250-dimensional
 # vector.
 print(u"Mean acceptance fraction: ", np.mean(sampler.acceptance_fraction))
-
-# Finally, you can plot the projected histograms of the samples using
-# matplotlib as follows (as long as you have it installed).
-try:
-    import matplotlib.pyplot as pl
-except ImportError:
-    print(u"Try installing matplotlib to generate some sweet plots...")
-else:
-    pl.hist(sampler.flatchain[:, 0], 100)
-    pl.show()
diff --git a/examples/quickstart.py b/examples/quickstart.py
index d67f8941..89f96510 100644
--- a/examples/quickstart.py
+++ b/examples/quickstart.py
@@ -46,7 +46,7 @@ def lnprob(x, mu, icov):
 sampler.run_mcmc(pos, 1000, rstate0=state)
 
 # Print out the mean acceptance fraction. In general, acceptance_fraction
-# has an entry for each walker so, in this case, it is a 100-dimensional
+# has an entry for each walker so, in this case, it is a 250-dimensional
 # vector.
 print("Mean acceptance fraction:", np.mean(sampler.acceptance_fraction))
 
@@ -67,4 +67,3 @@ def lnprob(x, mu, icov):
 else:
     pl.hist(sampler.flatchain[:,0], 100)
     pl.show()
-