Hari Subramoni

High performance RDMA-based design of HDFS over InfiniBand

Hadoop Distributed File System (HDFS) acts as the primary storage of Hadoop and has been adopted by reputed organizations (Facebook, Yahoo! etc.) due to its portability and fault-tolerance. The existing implementation of HDFS uses Javasocket interface for communication which delivers suboptimal performance in terms of latency and throughput. For dataintensive applications, network performance becomes key component as the amount of data being stored and replicated to HDFS increases. In this paper, we present a novel design of HDFS using Remote Direct Memory Access (RDMA) over InfiniBand via JNI interfaces. Experimental results show that, for 5GB HDFS file writes, the new design reduces the communication time by 87% and 30% over 1Gigabit Ethernet (1GigE) and IP-over-InfiniBand (IPoIB), respectively, on QDR platform (32Gbps). For HBase, the Put operation performance is improved by 26% with our design. To the best of our knowledge, this is the first design of HDFS over InfiniBand networks.

Memcached Design on High Performance RDMA Capable Interconnects

Memcached is a key-value distributed memory object caching system. It is used widely in the data-center environment for caching results of database calls, API calls or any other data. Using Memcached, spare memory in data-center servers can be aggregated to speed up lookups of frequently accessed information. The performance of Memcached is directly related to the underlying networking technology, as workloads are often latency sensitive. The existing Memcached implementation is built upon BSD Sockets interface. Sockets offers byte-stream oriented semantics. Therefore, using Sockets, there is a conversion between Memcacheds memory-object semantics and Sockets byte-stream semantics, imposing an overhead. This is in addition to any extra memory copies in the Sockets implementation within the OS. Over the past decade, high performance interconnects have employed Remote Direct Memory Access (RDMA) technology to provide excellent performance for the scientific computation domain. In addition to its high raw performance, the memory-based semantics of RDMA fits very well with Memcacheds memory-object model. While the Sockets interface can be ported to use RDMA, it is not very efficient when compared with low-level RDMA APIs. In this paper, we describe a novel design of Memcached for RDMA capable networks. Our design extends the existing open-source Memcached software and makes it RDMA capable. We provide a detailed performance comparison of our Memcached design compared to unmodified Memcached using Sockets over RDMA and 10Gigabit Ethernet network with hardware-accelerated TCP/IP. Our performance evaluation reveals that latency of Memcached Get of 4KB size can be brought down to 12 µs using ConnectX InfiniBand QDR adapters. Latency of the same operation using older generation DDR adapters is about 20µs. These numbers are about a factor of four better than the performance obtained by using 10GigE with TCP Offload. In addition, these latencies of Get requests over a range of message sizes are better by a factor of five to ten compared to IP over InfiniBand and Sockets Direct Protocol over InfiniBand. Further, throughput of small Get operations can be improved by a factor of six when compared to Sockets over 10 Gigabit Ethernet network. Similar factor of six improvement in throughput is observed over Sockets Direct Protocol using ConnectX QDR adapters. To the best of our knowledge, this is the first such memcached design on high performance RDMA capable interconnects.

High-Performance Design of Hadoop RPC with RDMA over InfiniBand

Hadoop RPC is the basic communication mechanism in the Hadoop ecosystem. It is used with other Hadoop components like MapReduce, HDFS, and HBase in real world data-centers, e.g. Facebook and Yahoo!. However, the current Hadoop RPC design is built on Java sockets interface, which limits its potential performance. The High Performance Computing community has exploited high throughput and low latency networks such as InfiniBand for many years. In this paper, we first analyze the performance of current Hadoop RPC design by unearthing buffer management and communication bottlenecks, that are not apparent on the slower speed networks. Then we propose a novel design (RPCoIB) of Hadoop RPC with RDMA over InfiniBand networks. RPCoIB provides a JVM-bypassed buffer management scheme and utilizes message size locality to avoid multiple memory allocations and copies in data serialization and deserialization. Our performance evaluations reveal that the basic ping-pong latencies for varied data sizes are reduced by 42%-49% and 46%-50% compared with 10GigE and IPoIB QDR (32Gbps), respectively, while the RPCoIB design also improves the peak throughput by 82% and 64% compared with 10GigE and IPoIB. As compared to default Hadoop over IPoIB QDR, our RPCoIB design improves the performance of the Sort benchmark on 64 compute nodes by 15%, while it improves the performance of CloudBurst application by 10%. We also present thorough, integrated evaluations of our RPCoIB design with other research directions, which optimize HDFS and HBase using RDMA over InfiniBand. Compared with their best performance, we observe 10% improvement for HDFS-IB, and 24% improvement for HBase-IB. To the best of our knowledge, this is the first such design of the Hadoop RPC system over high performance networks such as InfiniBand.

High-Performance Design of HBase with RDMA over InfiniBand

HBase is an open source distributed Key/Value store based on the idea of Big Table. It is being used in many data-center Papplications (e.g. Face book, Twitter, etc.) because of its portability and massive scalability. For this kind of system, low latency and high throughput is expected when supporting services for large scale concurrent accesses. However, the existing HBase implementation is built upon Java Sockets Interface that provides sub-optimal performance due to the overhead to provide cross-platform portability. The byte-stream oriented Java sockets semantics confine the possibility to leverage new generations of network technologies. This makes it hard to provide high performance services for data-intensive applications. High Performance Computing (HPC) domain has exploited high performance and low latency networks such as Infini Band for many years. These interconnects provide advanced network features, such as Remote Direct Memory Access (RDMA), to achieve high throughput and low latency along with low CPU utilization. RDMA follows memory-block semantics, which can be adopted efficiently to satisfy the object transmission primitives used in HBase. In this paper, we present a novel design of HBase for RDMA capable networks via Java Native Interface (JNI). Our design extends the existing open-source HBase software and makes it RDMA capable. Our performance evaluation reveals that latency of HBase Get operations of 1KB message size can be reduced to 43.7μs with the new design on QDR platform (32 Gbps). This is about a factor of 3.5 improvement over 10 Gigabit Ethernet (10 GigE) network with TCP Offload. Throughput evaluations using four HBase region servers and 64 clients indicate that the new design boosts up throughput by 3 X times over 1 GigE and 10 GigE networks. To the best of our knowledge, this is first HBase design utilizing high performance RDMA capable interconnects.

Designing topology-aware collective communication algorithms for large scale InfiniBand clusters: Case studies with Scatter and Gather

Modern high performance computing systems are being increasingly deployed in a hierarchical fashion with multi-core computing platforms forming the base of the hierarchy. These systems are usually comprised of multiple racks, with each rack consisting of a finite number of chassis, and each chassis having multiple compute nodes or blades, based on multi-core architectures. The networks are also hierarchical with multiple levels of switches. Message exchange operations between processes that belong to different racks involve multiple hops across different switches and this directly affects the performance of collective operations. In this paper, we take on the challenges involved in detecting the topology of large scale InfiniBand clusters and leveraging this knowledge to design efficient topology-aware algorithms for collective operations. We also propose a communication model to analyze the communication costs involved in collective operations on large scale supercomputing systems. We have analyzed the performance characteristics of two collectives, MPI_Gather and MPI_Scatter, on such systems and we have proposed topology-aware algorithms for these operations. Our experimental results have shown that the proposed algorithms can improve the performance of these collective operations by almost 54% at the micro-benchmark level.

High-Performance RDMA-based Design of Hadoop MapReduce over InfiniBand

MapReduce is a very popular programming model used to handle large datasets in enterprise data centers and clouds. Although various implementations of MapReduce exist, Hadoop MapReduce is the most widely used in large data centers like Facebook, Yahoo! and Amazon due to its portability and fault tolerance. Network performance plays a key role in determining the performance of data intensive applications using Hadoop MapReduce as data required by the map and reduce processes can be distributed across the cluster. In this context, data center designers have been looking at high performance interconnects such as InfiniBand to enhance the performance of their Hadoop MapReduce based applications. However, achieving better performance through usage of high performance interconnects like InfiniBand is a significant task. It requires a careful redesign of communication framework inside MapReduce. Several assumptions made for current socket based communication in the current framework do not hold true for high performance interconnects. In this paper, we propose the design of an RDMA-based Hadoop MapReduce over InfiniBand and several design elements: data shuffle over InfiniBand, in-memory merge mechanism for the Reducer, and pre-fetch data for the Mapper. We perform our experiments on native InfiniBand using Remote Direct Memory Access (RDMA) and compare our results with that of Hadoop-A [1] and default Hadoop over different interconnects and protocols. For all these experiments, we perform network level parameter tuning and use optimum values for each Hadoop design. Our performance results show that, for a 100GB TeraSort running on an eight node cluster, we achieve a performance improvement of 32% over IP-over InfiniBand (IPoIB) and 21% over Hadoop-A. With multiple disks per node, this benefit rises up to 39% over IPoIB and 31% over Hadoop-A.

High-performance and scalable non-blocking all-to-all with collective offload on InfiniBand clusters: a study with parallel 3D FFT

Three-dimensional FFT is an important component of many scientific computing applications ranging from fluid dynamics, to astrophysics and molecular dynamics. P3DFFT is a widely used three-dimensional FFT package. It uses the Message Passing Interface (MPI) programming model. The performance and scalability of parallel 3D FFT is limited by the time spent in the Alltoall Personalized exchange (MPI_Alltoall) operations. Hiding the latency of the MPI_Alltoall operation is critical towards scaling P3DFFT. The newest revision of MPI, MPI-3, is widely expected to provide support for non-blocking collective communication to enable latency-hiding. The latest InfiniBand adapter from Mellanox, ConnectX-2, enables offloading of generalized lists of communication operations to the network interface. Such an interface can be leveraged to design non-blocking collective operations. In this paper, we design a scalable, non-blocking Alltoall Personalized Exchange algorithm based on the network offload technology. To the best of our knowledge, this is the first paper to propose high performance non-blocking algorithms for dense collective operations, by leveraging InfiniBand’s network offload features. We also re-design the P3DFFT library and a sample application kernel to overlap the Alltoall operations with application-level computation. We are able to scale our implementation of the non-blocking Alltoall operation to more than 512 processes and we achieve near perfect computation/communication overlap (99%). We also see an improvement of about 23% in the overall run-time of our modified P3DFFT when compared to the default-blocking version and an improvement of about 17% when compared to the host-based non-blocking Alltoall schemes.

Design of a scalable InfiniBand topology service to enable network-topology-aware placement of processes

Over the last decade, InfiniBand has become an increasingly popular interconnect for deploying modern supercomputing systems. However, there exists no detection service that can discover the underlying network topology in a scalable manner and expose this information to runtime libraries and users of the high performance computing systems in a convenient way. In this paper, we design a novel and scalable method to detect the InfiniBand network topology by using Neighbor-Joining techniques (NJ). To the best of our knowledge, this is the first instance where the neighbor joining algorithm has been applied to solve the problem of detecting InfiniBand network topology. We also design a network-topology-aware MPI library that takes advantage of the network topology service. The library places processes taking part in the MPI job in a network-topology-aware manner with the dual aim of increasing intra-node communication and reducing the long distance inter-node communication across the InfiniBand fabric.

Scalable Memcached Design for InfiniBand Clusters Using Hybrid Transports

Mem cached is a general-purpose key-value based distributed memory object caching system. It is widely used in data-center domain for caching results of database calls, API calls or page rendering. An efficient Mem cached design is critical to achieve high transaction throughput and scalability. Previous research in the field has shown that the use of high performance interconnects like InfiniBand can dramatically improve the performance of Mem cached. The Reliable Connection (RC) is the most commonly used transport model for InfiniBand implementations. However, it has been shown that RC transport imposes scalability issues due to high memory consumption per connection. Such a characteristic is not favorable for middle wares like Mem cached, where the server is required to serve thousands of clients. The Unreliable Datagram (UD) transport offers higher scalability, but has several other limitations, which need to be efficiently handled. In this context, we introduce a hybrid transport model which takes advantage of the best features of RC and UD to deliver scalability and performance higher than that of a single-transport. To the best of our knowledge, this is the first effort aimed at studying the impact of using a hybrid of multiple transport protocols on Mem cached performance. We present comprehensive performance analysis using micro benchmarks, application benchmarks and realistic industry workloads. Our performance evaluations reveal that our Hybrid transport delivers performance comparable to that of RC, while maintaining a steady memory footprint. Mem cached Get latency for 4byte data size, is 4.28μs and 4.86μs for RC and hybrid transports, respectively. This represents a factor of twelve improvement over the performance of SDP. In evaluations using Apache Olio benchmark with 1,024 clients, Mem cached execution time using RC, UD and hybrid transports are 1.61, 1.96 and 1.70 seconds, respectively. Further, our scalability analysis with 4,096 client connections reveal that our proposed hybrid transport achieves good memory scalability.

Design and evaluation of benchmarks for financial applications using Advanced Message Queuing Protocol (AMQP) over InfiniBand

Message oriented middleware (MOM) is a key technology in financial market data delivery. In this context we study the advanced message queuing protocol (AMQP), an emerging open standard for MOM communication. We design a basic suite of benchmarks for AMQPpsilas Direct, Fanout, and Topic Exchange types. We then evaluate these benchmarks with Apache Qpid, an open source implementation of AMQP. In order to observe how AMQP performs in a real-life scenario, we also perform evaluations with a simulated stock exchange application. All our evaluations are performed over InfiniBand as well as 1 Gigabit Ethernet networks. Our results indicate that in order to achieve the high scalability requirements demanded by high performance computational finance applications, we need to use modern communication protocols, like RDMA, which place less processing load on the host. We also find that the centralized architecture of AMQP presents a considerable bottleneck as far as scalability is concerned.