Congestion control plays a pivotal role in large-scale data centers, facilitating ultra-low latency, high bandwidth, and optimal utilization. Even with the deployment of data center congestion control mechanisms such as DCQCN and HPCC, these algorithms often respond to congestion sluggishly. This sluggishness is primarily due to the slow notification of congestion. It takes almost one round-trip time (RTT) for the congestion information to reach the sender. In this paper, we introduce the Fast Notification Congestion Control (FNCC) mechanism, which achieves sub-RTT notification. FNCC leverages the acknowledgment packet (ACK) from the return path to carry in-network telemetry (INT) information of the request path, offering the sender more timely and accurate INT. To further accelerate the responsiveness of last-hop congestion control, we propose that the receiver notifies the sender of the number of concurrent congested flows, which can be used to adjust the congested flows to a fair rate quickly. Our experimental results demonstrate that FNCC reduces flow completion time by 27.4% and 88.9% compared to HPCC and DCQCN, respectively. Moreover, FNCC triggers minimal pause frames and maintains high utilization even at 400Gbps.

Physical phenomenon such as protein folding requires simulation up to microseconds of physical time, which directly corresponds to the strong scaling of molecular dynamics(MD) on modern supercomputers. In this paper, we present a highly scalable implementation of the state-of-the-art MD code LAMMPS on Fugaku by exploiting the 6D mesh/torus topology of the TofuD network. Based on our detailed analysis of the MD communication pattern, we first adapt coarse-grained peer-to-peer ghost-region communication with uTofu interface, then further improve the scalability via fine-grained thread pool. Finally, Remote direct memory access (RDMA) primitives are utilized to avoid buffer overhead. Numerical results show that our optimized code can reduce 77% of the communication time, improving the performance of baseline LAMMPS by a factor of 2.9x and 2.2x for Lennard-Jones and embedded-atom method potentials when scaling to 36, 846 computing nodes. Our optimization techniques can also benefit other applications with stencil or domain decomposition methods.

Congestion control is vital for achieving ultra-low latency and ultra-high throughput in large-scale data centers. However, existing mechanisms like DCQCN and HPCC often respond slowly and inaccurately to congestion, leading to extended queuing delays and reduced throughput. The notification delay of nearly one round-trip time (RTT) exacerbates congestion issues. Furthermore, these algorithms fail to identify queue growth caused by priority flow control (PFC) pause frames from the downstream device in lossless networks, resulting in unnecessary rate reductions on innocent flows. In this paper, we introduce the ACK-based Fast Congestion Control (AFCC) mechanism, designed to achieve sub-RTT notification delays by leveraging ACK packets. To enhance responsiveness to last-hop congestion, AFCC incorporates the number of concurrent congested flows within each ACK. Furthermore, AFCC utilizes ACK’s In-band Network Telemetry (INT) to identify the paused phase, thereby facilitating accurate congestion detection. Experimental results show that AFCC effectively maintains high utilization and significantly reduces flow completion time by 31.2% compared to HPCC and 78.4% compared to DCQCN.