425 research outputs found
Optimized Broadcast for Deep Learning Workloads on Dense-GPU InfiniBand Clusters: MPI or NCCL?
Dense Multi-GPU systems have recently gained a lot of attention in the HPC
arena. Traditionally, MPI runtimes have been primarily designed for clusters
with a large number of nodes. However, with the advent of MPI+CUDA applications
and CUDA-Aware MPI runtimes like MVAPICH2 and OpenMPI, it has become important
to address efficient communication schemes for such dense Multi-GPU nodes. This
coupled with new application workloads brought forward by Deep Learning
frameworks like Caffe and Microsoft CNTK pose additional design constraints due
to very large message communication of GPU buffers during the training phase.
In this context, special-purpose libraries like NVIDIA NCCL have been proposed
for GPU-based collective communication on dense GPU systems. In this paper, we
propose a pipelined chain (ring) design for the MPI_Bcast collective operation
along with an enhanced collective tuning framework in MVAPICH2-GDR that enables
efficient intra-/inter-node multi-GPU communication. We present an in-depth
performance landscape for the proposed MPI_Bcast schemes along with a
comparative analysis of NVIDIA NCCL Broadcast and NCCL-based MPI_Bcast. The
proposed designs for MVAPICH2-GDR enable up to 14X and 16.6X improvement,
compared to NCCL-based solutions, for intra- and inter-node broadcast latency,
respectively. In addition, the proposed designs provide up to 7% improvement
over NCCL-based solutions for data parallel training of the VGG network on 128
GPUs using Microsoft CNTK.Comment: 8 pages, 3 figure
Design of efficient Java message-passing collectives on multi-core clusters
This is a post-peer-review, pre-copyedit version of an article published in The Journal of Supercomputing. The final authenticated version is available online at: https://doi.org/10.1007/s11227-010-0464-5[Abstract] This paper presents a scalable and efficient Message-Passing in Java (MPJ) collective communication library for parallel computing on multi-core architectures. The continuous increase in the number of cores per processor underscores the need for scalable parallel solutions. Moreover, current system deployments are usually multi-core clusters, a hybrid shared/distributed memory architecture which increases the complexity of communication protocols. Here, Java represents an attractive choice for the development of communication middleware for these systems, as it provides built-in networking and multithreading support. As the gap between Java and compiled languages performance has been narrowing for the last years, Java is an emerging option for High Performance Computing (HPC).
Our MPJ collective communication library increases Java HPC applications performance on multi-core clusters: (1) providing multi-core aware collective primitives; (2) implementing several algorithms (up to six) per collective operation, whereas publicly available MPJ libraries are usually restricted to one algorithm; (3) analyzing the efficiency of thread-based collective operations; (4) selecting at runtime the most efficient algorithm depending on the specific multi-core system architecture, and the number of cores and message length involved in the collective operation; (5) supporting the automatic performance tuning of the collectives depending on the system and communication parameters; and (6) allowing its integration in any MPJ implementation as it is based on MPJ point-to-point primitives. A performance evaluation on an InfiniBand and Gigabit Ethernet multi-core cluster has shown that the implemented collectives significantly outperform the original ones, as well as higher speedups when analyzing the impact of their use on collective communications intensive Java HPC applications. Finally, the presented library has been successfully integrated in MPJ Express (http://mpj-express.org), and will be distributed with the next release.Ministerio de Ciencia e Innovación; TIN2010-16735Ministerio de Educación; FPU; AP2009-2112Xunta de Galicia; PGIDIT06PXIB105228P
A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures
Scientific problems that depend on processing large amounts of data require
overcoming challenges in multiple areas: managing large-scale data
distribution, co-placement and scheduling of data with compute resources, and
storing and transferring large volumes of data. We analyze the ecosystems of
the two prominent paradigms for data-intensive applications, hereafter referred
to as the high-performance computing and the Apache-Hadoop paradigm. We propose
a basis, common terminology and functional factors upon which to analyze the
two approaches of both paradigms. We discuss the concept of "Big Data Ogres"
and their facets as means of understanding and characterizing the most common
application workloads found across the two paradigms. We then discuss the
salient features of the two paradigms, and compare and contrast the two
approaches. Specifically, we examine common implementation/approaches of these
paradigms, shed light upon the reasons for their current "architecture" and
discuss some typical workloads that utilize them. In spite of the significant
software distinctions, we believe there is architectural similarity. We discuss
the potential integration of different implementations, across the different
levels and components. Our comparison progresses from a fully qualitative
examination of the two paradigms, to a semi-quantitative methodology. We use a
simple and broadly used Ogre (K-means clustering), characterize its performance
on a range of representative platforms, covering several implementations from
both paradigms. Our experiments provide an insight into the relative strengths
of the two paradigms. We propose that the set of Ogres will serve as a
benchmark to evaluate the two paradigms along different dimensions.Comment: 8 pages, 2 figure
Improving the Performance of the MPI_Allreduce Collective Operation through Rank Renaming
Proceedings of: First International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2014). Porto (Portugal), August 27-28, 2014.Collective operations, a key issue in the global efficiency of HPC applications, are optimized in current MPI libraries by choosing at runtime between a set of algorithms, based on platform-dependent beforehand established parameters, as the message size or the number of processes. However, with progressively more cores per node, the cost of a collective algorithm must be mainly imputed to process-to-processor mapping, because its decisive influence over the network traffic. Hierarchical design of collective algorithms pursuits to minimize the data movement through the slowest communication channels of the multi-core cluster. Nevertheless, the hierarchical implementation of some collectives becomes inefficient, and even impracticable, due to the operation definition itself. This paper proposes a new approach that departs from a frequently found regular mapping, either sequential or round-robin. While keeping the mapping, the rank assignation to the processes is temporarily changed prior to the execution of the collective algorithm. The new assignation makes the communication pattern to adapt to the communication channels hierarchy. We explore this technique for the Ring algorithm when used in the well-known MPI_Allreduce collective, and discuss the obtained performance results. Extensions to other algorithms and collective operations are proposed.The work presented in this paper has been partially supported by EU
under the COST programme Action IC1305, ’Network for Sustainable
Ultrascale Computing (NESUS)’, and by the computing facilities
of Extremadura Research Centre for Advanced Technologies (CETACIEMAT),
funded by the European Regional Development Fund
(ERDF). CETA-CIEMAT belongs to CIEMAT and the Government of
Spain
Nonblocking collectives for scalable Java communications
This is the peer reviewed version of the following article: Ramos, S., Taboada, G. L., Expósito, R. R., & Touriño, J. (2015). Nonblocking collectives for scalable Java communications. Concurrency and Computation: Practice and Experience, 27(5), 1169-1187, which has been published in final form at https://doi.org/10.1002/cpe.3279. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.[Abstract] This paper presents a Java implementation of the recently published MPI 3.0 nonblocking message passing collectives in order to analyze and assess the feasibility of taking advantage of these operations in shared memory systems using Java. Nonblocking collectives aim to exploit the overlapping between computation and communication for collective operations to increase scalability of message passing codes, as it has been carried out for nonblocking point‐to‐point primitives. This scalability has become crucial not only for clusters but also for shared memory systems because of the current trend of increasing the number of cores per chip, which is leading to the generalization of multi‐core and many‐core processors. Message passing libraries based on remote direct memory access, thread‐based progression, or implementing pure multi‐threading shared memory support could potentially benefit from the lack of imposed synchronization by nonblocking collectives. But, although the distributed memory scenario has been well studied, the shared memory one has not been tackled yet. Hence, nonblocking collectives support has been included in FastMPJ, a Message Passing in Java (MPJ) implementation, and evaluated on a representative shared memory system, obtaining significant improvements because of overlapping and lack of implicit synchronization, and with barely any overhead imposed over common blocking operations.Ministerio de Ciencia e Innovación; TIN2010-16735Xunta de Galicia; CN2012/211Xunta de Galicia; GRC2013/05
Optimizing Irregular Communication with Neighborhood Collectives and Locality-Aware Parallelism
Irregular communication often limits both the performance and scalability of
parallel applications. Typically, applications individually implement irregular
messages using point-to-point communications, and any optimizations are added
directly into the application. As a result, these optimizations lack
portability. There is no easy way to optimize point-to-point messages within
MPI, as the interface for single messages provides no information on the
collection of all communication to be performed. However, the persistent
neighbor collective API, released in the MPI 4 standard, provides an interface
for portable optimizations of irregular communication within MPI libraries.
This paper presents methods for optimizing irregular communication within
neighborhood collectives, analyzes the impact of replacing point-to-point
communication in existing codebases such as Hypre BoomerAMG with neighborhood
collectives, and finally shows an up to 1.32x speedup on sparse matrix-vector
multiplication within a BoomerAMG solve through the use of our optimized
neighbor collectives. The authors analyze multiple implementations of
neighborhood collectives, including a standard implementation, which simply
wraps standard point-to-point communication, as well as multiple
implementations of locality-aware aggregation. All optimizations are available
in an open-source codebase, MPI Advance, which sits on top of MPI, allowing for
optimizations to be added into existing codebases regardless of the system MPI
install
Accelerating MPI collective communications through hierarchical algorithms with flexible inter-node communication and imbalance awareness
This work presents and evaluates algorithms for MPI collective communication operations on high performance systems. Collective communication algorithms are extensively investigated, and a universal algorithm to improve the performance of MPI collective operations on hierarchical clusters is introduced. This algorithm exploits shared-memory buffers for efficient intra-node communication while still allowing the use of unmodified, hierarchy-unaware traditional collectives for inter-node communication. The universal algorithm shows impressive performance results with a variety of collectives, improving upon the MPICH algorithms as well as the Cray MPT algorithms. Speedups average 15x - 30x for most collectives with improved scalability up to 65536 cores.^ Further novel improvements are also proposed for inter-node communication. By utilizing algorithms which take advantage of multiple senders from the same shared memory buffer, an additional speedup of 2.5x can be achieved. The discussion also evaluates special-purpose extensions to improve intra-node communication. These extensions return a shared memory or copy-on-write protected buffer from the collective, which reduces or completely eliminates the second phase of intra-node communication.^ The second part of this work improves the performance of MPI collective communication operations in the presence of imbalanced processes arrival times. High performance collective communications are crucial for the performance and scalability of applications, and imbalanced process arrival times are common in these applications. A micro-benchmark is used to investigate the nature of process imbalance with perfectly balanced workloads, and understand the nature of inter- versus intra-node imbalance. These insights are then used to develop imbalance tolerant reduction, broadcast, and alltoall algorithms, which minimize the synchronization delay observed by early arriving processes. These algorithms have been implemented and tested on a Cray XE6 using up to 32k cores with varying buffer sizes and levels of imbalance. Results show speedups over MPICH averaging 18.9x for reduce, 5.3x for broadcast, and 6.9x for alltoall in the presence of high, but not unreasonable, imbalance
Scalable Distributed DNN Training using TensorFlow and CUDA-Aware MPI: Characterization, Designs, and Performance Evaluation
TensorFlow has been the most widely adopted Machine/Deep Learning framework.
However, little exists in the literature that provides a thorough understanding
of the capabilities which TensorFlow offers for the distributed training of
large ML/DL models that need computation and communication at scale. Most
commonly used distributed training approaches for TF can be categorized as
follows: 1) Google Remote Procedure Call (gRPC), 2) gRPC+X: X=(InfiniBand
Verbs, Message Passing Interface, and GPUDirect RDMA), and 3) No-gRPC: Baidu
Allreduce with MPI, Horovod with MPI, and Horovod with NVIDIA NCCL. In this
paper, we provide an in-depth performance characterization and analysis of
these distributed training approaches on various GPU clusters including the Piz
Daint system (6 on Top500). We perform experiments to gain novel insights along
the following vectors: 1) Application-level scalability of DNN training, 2)
Effect of Batch Size on scaling efficiency, 3) Impact of the MPI library used
for no-gRPC approaches, and 4) Type and size of DNN architectures. Based on
these experiments, we present two key insights: 1) Overall, No-gRPC designs
achieve better performance compared to gRPC-based approaches for most
configurations, and 2) The performance of No-gRPC is heavily influenced by the
gradient aggregation using Allreduce. Finally, we propose a truly CUDA-Aware
MPI Allreduce design that exploits CUDA kernels and pointer caching to perform
large reductions efficiently. Our proposed designs offer 5-17X better
performance than NCCL2 for small and medium messages, and reduces latency by
29% for large messages. The proposed optimizations help Horovod-MPI to achieve
approximately 90% scaling efficiency for ResNet-50 training on 64 GPUs.
Further, Horovod-MPI achieves 1.8X and 3.2X higher throughput than the native
gRPC method for ResNet-50 and MobileNet, respectively, on the Piz Daint
cluster.Comment: 10 pages, 9 figures, submitted to IEEE IPDPS 2019 for peer-revie
MPI Collectives for Multi-core Clusters: Optimized Performance of the Hybrid MPI+MPI Parallel Codes
The advent of multi-/many-core processors in clusters advocates hybrid
parallel programming, which combines Message Passing Interface (MPI) for
inter-node parallelism with a shared memory model for on-node parallelism.
Compared to the traditional hybrid approach of MPI plus OpenMP, a new, but
promising hybrid approach of MPI plus MPI-3 shared-memory extensions (MPI+MPI)
is gaining attraction. We describe an algorithmic approach for collective
operations (with allgather and broadcast as concrete examples) in the context
of hybrid MPI+MPI, so as to minimize memory consumption and memory copies. With
this approach, only one memory copy is maintained and shared by on-node
processes. This allows the removal of unnecessary on-node copies of replicated
data that are required between MPI processes when the collectives are invoked
in the context of pure MPI. We compare our approach of collectives for hybrid
MPI+MPI and the traditional one for pure MPI, and also have a discussion on the
synchronization that is required to guarantee data integrity. The performance
of our approach has been validated on a Cray XC40 system (Cray MPI) and NEC
cluster (OpenMPI), showing that it achieves comparable or better performance
for allgather operations. We have further validated our approach with a
standard computational kernel, namely distributed matrix multiplication, and a
Bayesian Probabilistic Matrix Factorization code.Comment: 10 pages. Accepted for publication in ICPP Workshops 201
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