322 research outputs found
Adaptive Routing Strategies for Modern High Performance Networks
Today’s scalable high-performance applications heavily depend on the bandwidth characteristics of their commu-nication patterns. Contemporary multi-stage interconnec-tion networks suffer from network contention which might decrease application performance. Our experiments show that the effective bisection bandwidth of a non-blocking 512-node Clos network is as low as 38 % if the network is routed statically. In this paper, we propose and ana-lyze different adaptive routing schemes for those networks. We chose Myrinet/MX to implement our proposed routing schemes. Our best adaptive routing scheme is able to in-crease the effective bisection bandwidth to 77 % for 512 nodes and 100 % for smaller node counts. Thus, we show that our proposed adaptive routing schemes are able to im-prove network throughput significantly.
Randomized Local Fast Rerouting for Datacenter Networks with Almost Optimal Congestion
To ensure high availability, datacenter networks must rely on local fast
rerouting mechanisms that allow routers to quickly react to link failures, in a
fully decentralized manner. However, configuring these mechanisms to provide a
high resilience against multiple failures while avoiding congestion along
failover routes is algorithmically challenging, as the rerouting rules can only
depend on local failure information and must be defined ahead of time. This
paper presents a randomized local fast rerouting algorithm for Clos networks,
the predominant datacenter topologies. Given a graph describing a
Clos topology, our algorithm defines local routing rules for each node , which only depend on the packet's destination and are conditioned on the
incident link failures. We prove that as long as number of failures at each
node does not exceed a certain bound, our algorithm achieves an asymptotically
minimal congestion up to polyloglog factors along failover paths. Our lower
bounds are developed under some natural routing assumptions
FatPaths: Routing in Supercomputers and Data Centers when Shortest Paths Fall Short
We introduce FatPaths: a simple, generic, and robust routing architecture
that enables state-of-the-art low-diameter topologies such as Slim Fly to
achieve unprecedented performance. FatPaths targets Ethernet stacks in both HPC
supercomputers as well as cloud data centers and clusters. FatPaths exposes and
exploits the rich ("fat") diversity of both minimal and non-minimal paths for
high-performance multi-pathing. Moreover, FatPaths uses a redesigned "purified"
transport layer that removes virtually all TCP performance issues (e.g., the
slow start), and incorporates flowlet switching, a technique used to prevent
packet reordering in TCP networks, to enable very simple and effective load
balancing. Our design enables recent low-diameter topologies to outperform
powerful Clos designs, achieving 15% higher net throughput at 2x lower latency
for comparable cost. FatPaths will significantly accelerate Ethernet clusters
that form more than 50% of the Top500 list and it may become a standard routing
scheme for modern topologies
CD-Xbar : a converge-diverge crossbar network for high-performance GPUs
Modern GPUs feature an increasing number of streaming multiprocessors (SMs) to boost system throughput. How to construct an efficient and scalable network-on-chip (NoC) for future high-performance GPUs is particularly critical. Although a mesh network is a widely used NoC topology in manycore CPUs for scalability and simplicity reasons, it is ill-suited to GPUs because of the many-to-few-to-many traffic pattern observed in GPU-compute workloads. Although a crossbar NoC is a natural fit, it does not scale to large SM counts while operating at high frequency. In this paper, we propose the converge-diverge crossbar (CD-Xbar) network with round-robin routing and topology-aware concurrent thread array (CTA) scheduling. CD-Xbar consists of two types of crossbars, a local crossbar and a global crossbar. A local crossbar converges input ports from the SMs into so-called converged ports; the global crossbar diverges these converged ports to the last-level cache (LLC) slices and memory controllers. CD-Xbar provides routing path diversity through the converged ports. Round-robin routing and topology-aware CTA scheduling balance network traffic among the converged ports within a local crossbar and across crossbars, respectively. Compared to a mesh with the same bisection bandwidth, CD-Xbar reduces NoC active silicon area and power consumption by 52.5 and 48.5 percent, respectively, while at the same time improving performance by 13.9 percent on average. CD-Xbar performs within 2.9 percent of an idealized fully-connected crossbar. We further demonstrate CD-Xbar's scalability, flexibility and improved performance perWatt (by 17.1 percent) over state-of-the-art GPU NoCs which are highly customized and non-scalable
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