Topology-aware Collective Communication in In-Network Computing Enabled Network: Problem Statement and Requirements

Large scale supercomputing systems have witnessed significant growth in the recent history. At the heart of these systems are compute nodes based on modern multi-core architectures and high speed networks. These systems offer vast amounts of computing power and resources to application developers and are allowing scientific applications to scale out to tens of thousands of processes. These processes rely on Message Passing Interface (MPI) for information exchange and complete parallel computing. The hardware network in reality is a physical network, while the communication between processes that are independent of hardware devices is abstracted as a logical network. An important aspect of communication in parallel computing is the rational mapping between logical network and physical network. When INC is introduced, the network hardware can also join the process of collective communication., which in turn will impact the overall communication model. Therefore, In INC enabled large-scale clusters, the mapping rules need to be adjusted accordingly. In large scale clusters, the network contention can significantly impact the performance of applications when the processor allocation is scattered across different racks in the cluster. It is critical to discover the topology of such clusters and design collective message exchange algorithms that are aware of the topology in order to improve the overall performance of real-world applications. After introducing INC, the topology discovery algorithm should not be limited to factors such as network structure and bandwidth, but also consider factors such as INC capacities and computational load.

INC In-Network Computing MPI Message Passing Interface

The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in BCP 14 when, and only when, they appear in all capitals, as shown here.

In traditional mode, computing tasks are completed by computers and servers in the cluster, and after enabling INC, some of the computing tasks are transferred to network devices. As a result, for the same MPI primitive, compared to traditional mode, after enabling INC, the communication subjects in the logical topology can not only be mapped to computers, but also to network devices. At the same time, the implementation of certain MPI primitives based on INC may result in topological difference compared to traditional patterns. The current topology mapping mechanism does not consider the content above. How to use topology-aware algorithms to improve MPI primitive communication performance and reduce communication costs in large-scale clusters has been a hot research direction. presents efficient topology-aware algorithms for two collective communication primitives and proposed a communication model to analyze the communication overhead of large-scale cluster communication. In , a new scheduling mechanism and topology-aware algorithm are proposed from the perspective of improving network bandwidth utilization, and it was verified that the network bandwidth utilization rate of a single AllReduce operation can be increased by 1.72 times. But when INC is enabled, these topology detection algorithms will not only be limited to network characteristics such as bandwidth and communication overhead, but should simultaneously consider the computing and processing capabilies of network devices themselves. Hence, several problems are raised: * How to properly map the communication logical topology subjects to the INC enabled physical network subjects? * How will enabling INC change the logical network topologies of MPI primitives and what challenges will it bring? * How do we efficiently discover the topology of an INC enabled large scale cluster? * What are the challenges involved in designing efficient collective algorithms that are aware of the INC enabled network topology?

The topology mapping algorithm between logical and physical networks in large-scale clusters enabled by INC, as well as the topology-aware collective communication algorithms used to enhance cluster communication, need to meet the following requirements: * INC enabled communication entities in large-scale clusters MUST not only support mapping to computing nodes in physical network, but also supporting mapping to network devices in physical network. * After introducing INC, logical communication may change. MPI primitives, for example, AllReduce, may correspond to one or more logical topologies that support INC. However, from the aspect of computation results, the implementation of logical topology that supports INC MUST be equivalent to traditional methods. * Topology detection algorithms in large-scale clusters that enable INC not only need to consider network factors such as communication overhead and path bandwidth, but also consider the INC capability and computational load of network devices, such as SINC . * The topology-aware collective communication algorithm SHOULD consider the network path load as well as the impact of background traffic on cluster communication performance in INC enabled large-scale clusters. * A reasonable evaluation model for INC enabled large-scale cluster is REQUIRED, taking into account the factors such as connectivity status and computing capabilities in network devices. * The topology mapping algorithm and topology detection algorithm SHOULD support the fallback mechanism, which can remap the logical network to the traditional mode and achieve path detection after an INC failure.

TBD.