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This shows yet another advantage of cloud: simplified machine configuration.įinally, source your required settings (%source $PITON_ROOT/piton/piton_settings.bash)įor the verification run, we used the single tile setup for OpenPiton, the regression script ‘sims’ provided in the OpenPiton bundle and the ‘tile1_mini’ regression. The scripts provided to you in the github repo already take care of installing Icarus on the machines provisioned. Set up the simulator home (%export ICARUS_HOME=/usr). Set up the PITON_ROOT environment variable (%export PITON_ROOT=) Once downloaded, there are three simple setup tasks to perform: Instructions are provided in the github repo. The next step is to download the design and verification files. The first step in running the OpenPiton regression is to follow the steps outlined in the github repo and complete the process successfully. Auto-scaling of a SLURM cluster refers to the capability of the cluster manager to spin up nodes on demand and shut down nodes automatically after jobs are completed. SLURM provides functionality such as enabling user access to compute nodes, managing a queue of pending work, and a framework for starting and monitoring jobs. Simple Linux Utility for Resource Management or SLURM is an open-source, fault-tolerant and highly scalable cluster management and job scheduling system for Linux clusters. Icarus Verilog, sometimes known as iverilog, is an open-source Verilog simulation and synthesis tool. It’s wildly popular within the research community and comes with scripts for performing the typical steps in the design flow, including dynamic simulation, logic synthesis and physical synthesis.
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Developed at Princeton University, it’s scalable and portable and can scale up to 500-million cores. OpenPiton is the world’s first open-source, general-purpose, multithreaded manycore processor and framework. We did this using a variety of tools: We used the OpenPiton design verification scripts, Icarus Verilog Simulator, SLURM workload management solution and Google Cloud standard compute configurations. How organizations can effectively leverage the elasticity of cloud to build highly utilized technology infrastructure How verification teams can benefit from auto-scaling cloud clusters How simulation can accelerate with more compute We wanted to create an easy set up using Google Cloud technologies and open-source designs and solutions to showcase three key points:
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Mature RTL verification flows include static analysis (checks for design integrity without use of test vectors), formal property verification (mathematically proving or falsifying design properties), dynamic simulation (test vector-based simulation of actual designs) and emulation (a complex system that imitates the behavior of the final chip, especially useful to validate functionality of the software stack).ĭynamic simulation arguably takes up the most compute in any design team’s data center.
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RTL designers use Hardware Description Languages (HDL) such as SystemVerilog and VHDL to create a design which then goes through a series of tools. Register Transfer Level (RTL) design and modeling is one of the most time consuming steps in the design process, accounting for more than half the time needed in the entire design cycle. Chip designers with Electronic Design Automation (EDA) workloads share these goals, and can greatly benefit from using cloud.Ĭhip design and manufacturing includes several tools across the flow, with varied compute and memory footprints. Companies embark on modernizing their infrastructure in the cloud for three main reasons: 1) to accelerate product delivery 2) to reduce system downtime and 3) to enable innovation.