Towards Java-based HPC using the MVAPICH2 Library: Early Experiences

There has been sporadic interest in using Java for High Performance Computing (HPC) in the past. These earlier efforts have resulted in several Java Message Passing Interface (MPI) [1] libraries including mpiJava [2], FastMPJ [3], MPJ Express [4], and Java Open MPI [5]. In this paper, we present our efforts in designing and implementing Java bindings for the MVAPICH2 [6] library. The MVAPICH2 Java bindings (MVAPICH2-J) follow the same API as the Java Open MPI library. MVAPICH2-J also provides support for communicating direct New I/O (NIO) ByteBuffers and Java arrays. Direct ByteBuffers reside outside JVM heaps and are not subject to the garbage collection. The library implements and utilizes a buffering layer to explicitly manage memory to avoid creating buffers every time a Java array message is communicated. In order to evaluate the performance of MVAPICH2-J and other Java MPI libraries, we also designed and implemented OMB-J that is a Java extension to the popular OSU Micro-Benchmarks suite (OMB) [7]. OMB-J currently supports a range of bench-marks for evaluating point-to-point and collective communication primitives. We also added support for communicating direct ByteBuffers and Java arrays. Our evaluations reveal that at the OMB-J level, ByteBuffers are superior in performance due to the elimination of extra copying between the Java and the Java Native Interface (JNI) layer. MVAPICH2-J achieves similar performance to Java Open MPI for ByteBuffers in point-to-point communication primitives that is evaluated using latency and bandwidth benchmarks. For Java arrays, there is a slight overhead for MVAPICH2-J due to the use of the buffering layer. For the collective communication benchmarks, we observe good performance for MVAPICH2-J. Where, MVAPICH2-J fairs better than Java Open MPI with ByteBuffers by $a$ factor of 6.2 and 2.76 for broadcast and all reduce, respectively, on average for all messages sizes. And, using Java arrays, $2. 2\times$ and $1. 62\times$ on average for broadcast and allreduce, respectively. The collective communication performance is dictated by the performance of the respective native MPI libraries.