Strong scaling of general-purpose molecular dynamics simulations on GPUs

Programmable data plane hardware creates new opportunities for infusing intelligence into the network. This raises a fundamental question: what kinds of computation should be delegated to the network? In this paper, we discuss the opportunities and challenges for co-designing data center distributed systems with their network layer. We believe that the time has finally come for offloading part of their computation to execute in-network. However, in-network computation tasks must be judiciously crafted to match the limitations of the network machine architecture of programmable devices. With the help of our experiments on machine learning and graph analytics workloads, we identify that aggregation functions raise opportunities to exploit the limited computation power of networking hardware to lessen network congestion and improve the overall application performance. Moreover, as a proof-of-concept, we propose Daiet, a system that performs in-network data aggregation. Experimental results with an initial prototype show a large data reduction ratio (86.9%-89.3%) and a similar decrease in the workers' computation time.

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In-Network Computation is a Dumb Idea Whose Time Has Come

Training machine learning models in parallel is an increasingly important workload. We accelerate distributed parallel training by designing a communication primitive that uses a programmable switch dataplane to execute a key step of the training process. Our approach, SwitchML, reduces the volume of exchanged data by aggregating the model updates from multiple workers in the network. We co-design the switch processing with the end-host protocols and ML frameworks to provide an efficient solution that speeds up training by up to 5.5$\times$ for a number of real-world benchmark models.

Scaling Distributed Machine Learning with In-Network Aggregation

Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. Deep neural networks, along with advancements in classical machine learning and scalable general-purpose graphics processing unit (GPU) computing, have become critical components of artificial intelligence, enabling many of these astounding breakthroughs and lowering the barrier to adoption. Python continues to be the most preferred language for scientific computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries and clean high-level APIs. This survey offers insight into the field of machine learning with Python, taking a tour through important topics to identify some of the core hardware and software paradigms that have enabled it. We cover widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward.

Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence

Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. Deep neural networks, along with advancements in classical ML and scalable general-purpose GPU computing, have become critical components of artificial intelligence, enabling many of these astounding breakthroughs and lowering the barrier to adoption. Python continues to be the most preferred language for scientific computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries and clean high-level APIs. This survey offers insight into the field of machine learning with Python, taking a tour through important topics to identify some of the core hardware and software paradigms that have enabled it. We cover widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward.

Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence.

GPUs and accelerators have become ubiquitous in modern supercomputing systems. Scientific applications from a wide range of fields are being modified to take advantage of their compute power. However, data movement continues to be a critical bottleneck in harnessing the full potential of a GPU. Data in the GPU memory has to be moved into the host memory before it can be sent over the network. MPI libraries like MVAPICH2 have provided solutions to alleviate this bottleneck using techniques like pipelining. GPUDirect RDMA is a feature introduced in CUDA 5.0, that allows third party devices like network adapters to directly access data in GPU device memory, over the PCIe bus. NVIDIA has partnered with Mellanox to make this solution available for InfiniBand clusters. In this paper, we evaluate the first version of GPUDirect RDMA for InfiniBand and propose designs in MVAPICH2 MPI library to efficiently take advantage of this feature. We highlight the limitations posed by current generation architectures in effectively using GPUDirect RDMA and address these issues through novel designs in MVAPICH2. To the best of our knowledge, this is the first work to demonstrate a solution for internode GPU-to-GPU MPI communication using GPUDirect RDMA. Results show that the proposed designs improve the latency of internode GPU-to-GPU communication using MPI Send/MPI Recv by 69% and 32% for 4Byte and 128KByte messages, respectively. The designs boost the uni-directional bandwidth achieved using 4KByte and 64KByte messages by 2x and 35%, respectively. We demonstrate the impact of the proposed designs using two end-applications: LBMGPU and AWP-ODC. They improve the communication times in these applications by up to 35% and 40%, respectively.

Efficient Inter-node MPI Communication Using GPUDirect RDMA for InfiniBand Clusters with NVIDIA GPUs

Many modern clusters are being equipped with multiple GPUs per node to achieve better compute density and power efficiency. However, moving data in/out of GPUs continues to remain a major performance bottleneck. With CUDA 4.1, NVIDIA has introduced Inter-Process Communication (IPC) to address data movement overheads between processes using different GPUs connected to the same node. State-of-the-art MPI libraries like MVAPICH2 are being modified to allow application developers to use MPI calls directly over GPU device memory. This improves the programmability for application developers by removing the burden of dealing with complex data movement optimizations. In this paper, we propose efficient designs for intra-node MPI communication on multi-GPU nodes, taking advantage of IPC capabilities provided in CUDA. We also demonstrate how MPI one-sided communication semantics can provide better performance and overlap by taking advantage of IPC and the Direct Memory Access (DMA) engine on a GPU. We demonstrate the effectiveness of our designs using micro-benchmarks and an application. The proposed designs improve GPU-to-GPU MPI Send/Receive latency for 4MByte messages by 79% and achieve 4 times the bandwidth for the same message size. One-sided communication using Put and Active synchronization shows 74% improvement in latency for 4MByte message, compared to the existing Send/Receive based implementation. Our benchmark using Get and Passive Synchronization demonstrates that true asynchronous progress can be achieved using IPC and the GPU DMA engine. Our designs for two-sided and one-sided communication improve the performance of GPULBM, a CUDA implementation of Lattice Boltzmann Method for multiphase flows, by 16%, compared to the performance using existing designs in MVAPICH2. To the best of our knowledge, this is the first paper to provide a comprehensive solution for MPI two-sided and one-sided GPU-to-GPU communication within a node, using CUDA IPC.

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Optimizing MPI Communication on Multi-GPU Systems Using CUDA Inter-Process Communication

Designing high-performance and scalable applications on GPU clusters requires tackling several challenges. The key challenge is the separate host memory and device memory, which requires programmers to use multiple programming models, such as CUDA and MPI, to operate on data in different memory spaces. This challenge becomes more difficult to tackle when non-contiguous data in multidimensional structures is used by real-world applications. These challenges limit the programming productivity and the application performance. We propose the GPU-Aware MPI to support data communication from GPU to GPU using standard MPI. It unifies the separate memory spaces, and avoids explicit CPU-GPU data movement and CPU/GPU buffer management. It supports all MPI datatypes on device memory with two algorithms: a GPU datatype vectorization algorithm and a vector based GPU kernel data pack and unpack algorithm. A pipeline is designed to overlap the non-contiguous data packing and unpacking on GPUs, the data movement on the PCIe, and the RDMA data transfer on the network. We incorporate our design with the open-source MPI library MVAPICH2 and optimize a production application: the multiphase 3D LBM. Besides the increase of programming productivity, we observe up to 19.9 percent improvement in application-level performance on 64 GPUs of the Oakley supercomputer.

GPU-Aware MPI on RDMA-Enabled Clusters: Design, Implementation and Evaluation

Increased system size and a greater reliance on utilizing system parallelism to achieve computational needs, requires innovative system architectures to meet the simulation challenges. As a step towards a new network class of co-processors — intelligent network devices, which manipulate data traversing the data-center network, this paper describes the SHArP technology designed to offload collective operation processing to the network. This is implemented in Mellanox's SwitchIB-2 ASIC, using innetwork trees to reduce data from a group of sources, and to distribute the result. Multiple parallel jobs with several partially overlapping groups are supported each with several reduction operations in-flight. Large performance enhancements are obtained, with an improvement of a factor of 2.1 for an eight byte MPI_Allreduce() operation on 128 hosts, going from 6.01 to 2.83 microseconds. Pipelining is used for an improvement of a factor of 3.24 in the latency of a 4096 byte MPI_Allreduce() operations, declining from 46.93 to 14.48 microseconds.

Scalable hierarchical aggregation protocol (SHArP): a hardware architecture for efficient data reduction

General-Purpose Graphics Processing Units (GPGPUs) are becoming a common component of modern supercomputing systems Many MPI applications are being modified to take advantage of the superior compute potential offered by GPUs To facilitate this process, many MPI libraries are being extended to support MPI communication from GPU device memory However, there is lack of a standardized benchmark suite that helps users evaluate common communication models on GPU clusters and do a fair comparison for different MPI libraries In this paper, we extend the widely used OSU Micro-Benchmarks (OMB) suite with benchmarks that evaluate performance of point-point, multi-pair and collective MPI communication for different GPU cluster configurations Benefits of the proposed benchmarks for MVAPICH2 and OpenMPI libraries are illustrated

Devendar Bureddy

Papers

Efficient Inter-node MPI Communication Using GPUDirect RDMA for InfiniBand Clusters with NVIDIA GPUs

Optimizing MPI Communication on Multi-GPU Systems Using CUDA Inter-Process Communication

GPU-Aware MPI on RDMA-Enabled Clusters: Design, Implementation and Evaluation

Scalable hierarchical aggregation protocol (SHArP): a hardware architecture for efficient data reduction

OMB-GPU: a micro-benchmark suite for evaluating MPI libraries on GPU clusters