The delivery of computing services including storage, databases, networking, software, analytics, intelligence and more is moving from the cloud towards the edge; among all technical requirements, the edge needs to be able to natively support artificial intelligence processes to enable new technological paradigms, such as smart Factory 4.0, healthcare assisted living, smart cities and advanced robotics, for example In this paper, we will present the enabling technologies to will be developed within the "Big data processing and artificial intelligence at the network edge" (BRAINE) project, one of the largest joint activities in Europe in the area of edge computing enabling artificial intelligence. BRAINE covers the full stack of a system solution, from subsystem integration up to artificial intelligence service provisioning, and includes four use cases of direct impact in the European ecosystem.

This paper reviews advanced software architectures and technologies that support innetworking computing and improve the overall performance of data centers and high-performance computing clusters; the ability to converge software and hardware allows for new solutions, such as artificial intelligence, to be deployed massively.

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.

A distinguishing characteristic of OpenSHMEM compared to other PGAS programming model implementations is its support for atomic memory operations (AMOs). It provides a rich set of AMO interfaces supporting 32-bit and 64-bit datatypes. On most modern networks, network-implemented AMOs are known to outperform software-implemented AMOs. So, for achieving high-performance, an OpenSHMEM implementation should try to offload AMOs to the underlying network hardware when possible. Nevertheless, the challenge arises when (a) underlying hardware does not support full set of atomic operations, (b) more that one device is used, and (c) heterogeneous systems with multiple types of devices are involved. In this paper, we analyze the challenges and discuss potential solutions to address these challenges.

Hadoop is a popular open-source implementation of the MapReduce programming model for cloud computing. However, it faces a number of issues to achieve the best performance from the underlying system. These include a serialization barrier that delays the reduce phase, repetitive merges and disk access, and lack of capability to leverage latest high speed interconnects. We describe Hadoop-A, an acceleration framework that optimizes Hadoop with plugin components implemented in C++ for fast data movement, overcoming its existing limitations. A novel network-levitated merge algorithm is introduced to merge data without repetition and disk access. In addition, a full pipeline is designed to overlap the shuffle, merge and reduce phases. Our experimental results show that Hadoop-A doubles the data processing throughput of Hadoop, and reduces CPU utilization by more than 36%.

Sockets Direct Protocol (SDP) is a byte stream protocol that utilizes the capabilities of the InfiniBand fabric to transparently achieve performance gains for existing socket-based networked applications. In this paper we discuss an implementation of Zero Copy support for synchronous send()/recv() socket calls, that uses the remote DMA capability of InfiniBand for SDP data transfers. We added this support to the open-source implementation of SDP over InfiniBand. We evaluate this implementation over a 20 Gb/s InfiniBand link. We demonstrate scalability of Zero Copy and show its benefits for systems that utilize multiple socket connections in parallel. For example, enabling Zero Copy with 8 active connections yields a bandwidth growth from 630MB/s to 1360MB/s, at the same time reducing the CPU utilization by a factor often

Sockets Direct Protocol (SDP) is a byte stream protocol that utilizes the capabilities of the InfiniBand fabric to transparently achieve performance gains for existing socket-based networked applications. We implemented SDP stack for the Windows operating system that is fully interoperable with Linux. The paper describes the early experience with the implementation of the protocol stack. We go through the motivation, the main implementation architectural aspects and challenges. We present preliminary performance results. Running over 20Gb/s InfiniBand double data rate (DDR) links we observed bandwidth record of 1316MB/s.