test: reduce disk usage when running benchmarks in parallel

test/run_benchmarks.py

@@ -456,19 +456,59 @@ def run_single_benchmark(func_args):
     return result
 
 
+InQueueItem = namedtuple('InQueueItem', ['index', 'config'])
+OutQueueItem = namedtuple('OutQueueItem', ['index', 'result'])
+
+
+def run_parallel(configurations, output_base_dir, njobs, ignore_failures):
+    from multiprocessing import Process, Queue
+    import queue
+
+    def worker(in_queue, out_queue, out_dir):
+        while True:
+            in_item = in_queue.get()
+            if in_item is None:
+                return
+            result = run_single_benchmark((in_item.config, out_dir, ignore_failures))
+            out_queue.put(OutQueueItem(in_item.index, result))
+
+    if njobs == 0:
+        njobs = os.cpu_count()
+    print('Using {:d} parallel jobs'.format(njobs))
+
+    # use one directory per worker, as running each benchmark in separate directory
+    # takes too much disk space (~2GB per 100 benchmarks)
+    dir_pool = [os.path.join(output_base_dir, 'worker_%02d' % i) for i in range(njobs)]
+
+    in_queue, out_queue = Queue(), Queue()
+    workers = [Process(target=worker, args=(in_queue, out_queue, dir)) for dir in dir_pool]
+    for w in workers:
+        w.start()
+
+    # put all benchmark configurations with index to retrieve them in order
+    for i, config in enumerate(configurations):
+        in_queue.put(InQueueItem(i, config))
+
+    # send "finish signal" for each worker
+    for _ in workers:
+        in_queue.put(None)
+
+    # retrieve results in proper order
+    out_items = [out_queue.get() for _ in configurations]
+    results = [out.result for out in sorted(out_items, key=lambda o: o.index)]
+
+    for p in workers:
+        p.join()
+
+    return results
+
+
 def run_benchmarks(configurations, output_base_dir, njobs, ignore_failures):
     print('Running {:d} benchmarks ...'.format(len(configurations)))
     if njobs == 1:
         results = [run_single_benchmark((config, output_base_dir, ignore_failures)) for config in configurations]
     else:
-        import multiprocessing
+        results = run_parallel(configurations, output_base_dir, njobs, ignore_failures)
-        func_args = [(config, os.path.join(output_base_dir, config.name.replace(' ', '_')), ignore_failures)
-                     for config in configurations]
-        if njobs == 0:
-            njobs = os.cpu_count()
-        print('Using {:d} parallel jobs'.format(njobs))
-        with multiprocessing.Pool(processes=njobs) as pool:
-            results = pool.map(run_single_benchmark, func_args)
     run_data = [RunCache.RunData(config, result) for config, result in zip(configurations, results)]
     return run_data