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Gao, Kang ; Meng, Xiangbo ; Laneman, J. Nicholas ; Chisum, Jonathan D. ; Bendlin, Ralf ; Chopra, Aditya ; Hochwald, Bertrand M. ( , IEEE Transactions on Communications)
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Karandikar, Sagar ; Mao, Howard ; Kim, Donggyu ; Biancolin, David ; Amid, Alon ; Lee, Dayeol ; Pemberton, Nathan ; Amaro, Emmanuel ; Schmidt, Colin ; Chopra, Aditya ; et al ( , 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA))We present FireSim, an open-source simulation platform that enables cycle-exact microarchitectural simulation of large scale-out clusters by combining FPGA-accelerated simulation of silicon-proven RTL designs with a scalable, distributed network simulation. Unlike prior FPGA-accelerated simulation tools, FireSim runs on Amazon EC2 F1, a public cloud FPGA platform, which greatly improves usability, provides elasticity, and lowers the cost of large-scale FPGA-based experiments. We describe the design and implementation of FireSim and show how it can provide sufficient performance to run modern applications at scale, to enable true hardware-software co-design. As an example, we demonstrate automatically generating and deploying a target cluster of 1,024 3.2 GHz quad-core server nodes, each with 16 GB of DRAM, interconnected by a 200 Gbit/s network with 2 microsecond latency, which simulates at a 3.4 MHz processor clock rate (less than 1,000x slowdown over real-time). In aggregate, this FireSim instantiation simulates 4,096 cores and 16 TB of memory, runs ~ 14 billion instructions per second, and harnesses 12.8 million dollars worth of FPGAs-at a total cost of only ~ $100 per simulation hour to the user. We present several examples to show how FireSim can be used to explore various research directions in warehouse-scale machine design, including modeling networks with high-bandwidth and low-latency, integrating arbitrary RTL designs for a variety of commodity and specialized datacenter nodes, and modeling a variety of datacenter organizations, as well as reusing the scale-out FireSim infrastructure to enable fast, massively parallel cycle-exact single-node microarchitectural experimentation.more » « less