BiNoCHS: Bimodal network-on-chip for CPU-GPU heterogeneous systems
BiNoCHS: Bimodal network-on-chip for CPU-GPU heterogeneous systems
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DOI：
10.1145/3130218.3130222
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发表时间：
2017-10
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通讯作者：
Amirhossein Mirhosseini;Mohammad Sadrosadati;Behnaz Soltani;H. Sarbazi-Azad;T. Wenisch
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作者：
Amirhossein Mirhosseini;Mohammad Sadrosadati;Behnaz Soltani;H. Sarbazi-Azad;T. Wenisch
CPU - GPU异构系统正成为高性能节能计算的首选架构。为此类系统设计片上互连具有挑战性；CPU通常从降低延迟的优化中获益匪浅，但很少使带宽或排队资源饱和。相比之下，GPU会产生大量流量，导致局部拥塞，从而影响CPU性能。针对拥塞进行优化的互连可以通过更大的虚拟和物理通道资源来缓解这一问题。然而，当流量较小时，由于较高的空载数据包延迟和关键路径延迟，此类网络变得次优。我们主张一种可重构网络，它能够在高负载/拥塞时激活额外的通道，并在网络空载时关闭它们。然而，这些额外的资源消耗更多功率，使得难以静态地为网络分配功率预算。我们引入了BiNoCHS，一种用于异构系统的可重构电压可调节片上网络。在CPU主导的低流量条件下，BiNoCHS以标称电压和高时钟频率运行，其拓扑结构针对低跳数进行了优化，从而使CPU性能最大化。在高流量GPU和混合工作负载下，它转换为近阈值模式，激活额外的路由器/通道以及非最小自适应路由来解决拥塞问题。我们的评估表明，在拥塞条件下，BiNoCHS相对于延迟优化网络将CPU / GPU性能提高了57% / 34%，而在空载条件下，相对于高带宽设计将CPU性能提高了28%。
CPU-GPU heterogeneous systems are emerging as architectures of choice for high-performance energy-efficient computing. Designing on-chip interconnects for such systems is challenging; CPUs typically benefit greatly from optimizations that reduce latency, but rarely saturate bandwidth or queueing resources. In contrast, GPUs generate intense traffic that produces local congestion, harming CPU performance. Congestion-optimized interconnects can mitigate this problem through larger virtual and physical channel resources. However, when there is little traffic, such networks become suboptimal due to higher unloaded packet latencies and critical path delays. We argue for a reconfigurable network that can activate additional channels under high load/congestion and shut them off when the network is unloaded. However, these additional resources consume more power, making it difficult to statically provision a power budget for the network. We introduce BiNoCHS, a reconfigurable voltage-scalable on-chip network for heterogeneous systems. Under CPU-dominated low-traffic conditions, BiNoCHS operates at nominal-voltage and high clock frequency with a topology optimized for low hop count, maximizing CPU performance. Under high-traffic GPU and mixed workloads, it transitions to a near-threshold mode, activating additional routers/channels and non-minimal adaptive routing to resolve congestion. Our evaluation shows that BiNoCHS improves CPU/GPU performance by 57% / 34% over a latency-optimized network under congested conditions, while improving CPU performance by 28% over high-bandwidth design in unloaded conditions.