diff --git a/algos.yaml b/algos.yaml
index 33970040f..8afdce7fd 100644
--- a/algos.yaml
+++ b/algos.yaml
@@ -26,8 +26,8 @@ float:
       base-args: ["@metric"]
       run-groups:
         base:
-          args: [[5, 10, 20, 50, 100, 200, 400, 800, 1000],
-                 [1, 2, 3, 4, 5, 8, 10, 20, 50, 100, 200]]
+          args: [[5, 10, 20, 50, 100, 200, 400, 800, 1000]]
+          query-args: [[1, 2, 3, 4, 5, 8, 10, 20, 50, 100, 200]]
     flann:
       docker-tag: ann-benchmarks-flann
       module: ann_benchmarks.algorithms.flann
diff --git a/ann_benchmarks/algorithms/faiss.py b/ann_benchmarks/algorithms/faiss.py
index 17b682f41..6bf975c44 100644
--- a/ann_benchmarks/algorithms/faiss.py
+++ b/ann_benchmarks/algorithms/faiss.py
@@ -38,11 +38,9 @@ def use_threads(self):
 import sklearn.preprocessing
 
 class FaissIVF(BaseANN):
-    def __init__(self, metric, n_list, n_probe):
+    def __init__(self, metric, n_list):
         self._n_list = n_list
-        self._n_probe = n_probe
         self._metric = metric
-        self.name = 'FaissIVF(n_list=%d, n_probe=%d)' % (self._n_list, self._n_probe)
 
     def fit(self, X):
         if self._metric == 'angular':
@@ -55,11 +53,17 @@ def fit(self, X):
         index = faiss.IndexIVFFlat(self.quantizer, X.shape[1], self._n_list, faiss.METRIC_L2)
         index.train(X)
         index.add(X)
-        index.nprobe = self._n_probe
         self._index = index
 
+    def set_query_arguments(self, n_probe):
+        self._n_probe = n_probe
+        self._index.n_probe = self._n_probe
+
     def query(self, v, n):
         if self._metric == 'angular':
             v /= numpy.linalg.norm(v)
         (dist,), (ids,) = self._index.search(v.reshape(1, -1).astype('float32'), n)
         return ids
+
+    def __str__(self):
+        return 'FaissIVF(n_list=%d, n_probe=%d)' % (self._n_list, self._n_probe)
diff --git a/ann_benchmarks/runner.py b/ann_benchmarks/runner.py
index d15d17d12..30835fa70 100644
--- a/ann_benchmarks/runner.py
+++ b/ann_benchmarks/runner.py
@@ -57,6 +57,7 @@ def batch_query(X):
         elif algo.use_threads() and not force_single:
             pool = multiprocessing.pool.ThreadPool()
             results = pool.map(single_query, X_test)
+            pool.close()
         else:
             results = [single_query(x) for x in X_test]