Make dist worker work with pipelining

cuburn/render.py

@@ -23,6 +23,20 @@ from cuburn.genome.util import palette_decode
 RenderedImage = namedtuple('RenderedImage', 'buf idx gpu_time')
 Dimensions = namedtuple('Dimensions', 'w h aw ah astride')
 
+class DurationEvent(cuda.Event):
+    """
+    A CUDA event which is implicitly aware of a prior event for time
+    calculations.
+
+    Note that instances retain a reference to their prior, so an unbroken
+    chain of DurationEvents will leak. Use normal events as priors.
+    """
+    def __init__(self, prior):
+        super(DurationEvent, self).__init__()
+        self._prior = prior
+    def time(self):
+        return self.time_since(self._prior)
+
 class Framebuffers(object):
     """
     The largest memory allocations, and a stream to serialize their use.
@@ -340,13 +354,17 @@ class RenderManager(ClsMod):
         leave ``copy`` to True every time for now.
 
         The return value is a 2-tuple ``(evt, h_out)``, where ``evt`` is a
-        CUDA event and ``h_out`` is the return value of the output module's
+        DurationEvent and ``h_out`` is the return value of the output module's
         ``copy`` function. In the typical case, ``h_out`` will be a host
         allocation containing data in an appropriate format for the output
         module's file writer, and ``evt`` indicates when the asynchronous
         DMA copy which will populate ``h_out`` is complete. This can vary
         depending on the output module in use, though.
+
+        This method is absolutely _not_ threadsafe, but it's fine to use it
+        alongside non-threaded approaches to concurrency like coroutines.
         """
+        timing_event = cuda.Event().record(self.stream_b)
         # Note: we synchronize on the previous stream if buffers need to be
         # reallocated, which implicitly also syncs the current stream.
         dim = self.fb.set_dim(gprof.width, gprof.height, self.stream_b)
@@ -372,7 +390,7 @@ class RenderManager(ClsMod):
         rdr.out.convert(self.fb, gprof, dim, self.stream_a)
         self.filt_evt = cuda.Event().record(self.stream_a)
         h_out = rdr.out.copy(self.fb, dim, self.fb.pool, self.stream_a)
-        self.copy_evt = cuda.Event().record(self.stream_a)
+        self.copy_evt = DurationEvent(timing_event).record(self.stream_a)
 
         self.info_a, self.info_b = self.info_b, self.info_a
         self.stream_a, self.stream_b = self.stream_b, self.stream_a

dist/worker.py

@@ -2,7 +2,9 @@
 import sys
 from cStringIO import StringIO
 
-import zmq
+import gevent
+from gevent import spawn, queue
+import zmq.green as zmq
 import pycuda.driver as cuda
 cuda.init()
 
@@ -19,31 +21,46 @@ class PrecompiledRenderer(render.Renderer):
         self.packer, self.cubin = packer, cubin
         super(PrecompiledRenderer, self).__init__(gnm, gprof)
 
-def main(card_num, worker_addr):
-    dev = cuda.Device(card_num)
-    ctx = dev.make_context(cuda.ctx_flags.SCHED_BLOCKING_SYNC)
+def main(worker_addr):
     rmgr = render.RenderManager()
 
     ctx = zmq.Context()
+    in_queue = queue.Queue(0)
+    out_queue = queue.Queue(0)
+
+    def request_loop():
         sock = ctx.socket(zmq.REQ)
         sock.connect(worker_addr)
 
         # Start the request loop with an empty job
         sock.send('')
+
         hash = None
         while True:
             addr, task, cubin, packer = sock.recv_pyobj()
             gprof = profile.wrap(task.profile, task.anim)
             if hash != task.hash:
                 rdr = PrecompiledRenderer(task.anim, gprof, packer, cubin)
-        buf = rmgr.queue_frame(rdr, task.anim, gprof, task.time)
+            evt, buf = rmgr.queue_frame(rdr, task.anim, gprof, task.time)
+            while not evt.query():
+                gevent.sleep(0.01)
             ofile = StringIO()
             output.PILOutput.save(buf, ofile, task.id[-3:])
             ofile.seek(0)
             sock.send_multipart(addr + [ofile.read()])
             hash = task.hash
-        print 'Rendered', task.id
+
+            print 'Rendered', task.id, 'in', int(evt.time()), 'ms'
+
+    # Spawn two request loops to take advantage of CUDA pipelining.
+    spawn(request_loop)
+    request_loop()
 
 if __name__ == "__main__":
     import addrs
-    main(int(sys.argv[1]), addrs.addrs['workers'])
+    dev = cuda.Device(int(sys.argv[1]))
+    cuctx = dev.make_context(cuda.ctx_flags.SCHED_BLOCKING_SYNC)
+    try:
+        main(addrs.addrs['workers'])
+    finally:
+        cuda.Context.pop()