Benchmarking a Raspberry Pi cluster

Benchmarking a Raspberry Pi cluster

Rigoberto Moreno Delgado was, like many other college students, unsure which path to go down. After taking a class on computer science, he decided to pursue it as his major, and became interested in parallel computing. “I began my journey into parallel and high-performance computing after my university advisor, Dr Ali Kooshesh, mentioned that my favourite professor, Dr Suzanne Rivoire, was teaching a course titled Parallel Computing,” Rigoberto explains to us. “I was sceptical about enrolling into the class since I had no idea what parallel computing was, but I decided to take the course and I did not regret my decision.” He met Dr Barry Rountree, a researcher at a lab that specialises in supercomputing, while working as an intern at the Lawrence Livermore National Laboratory (LLNL) over the summer of 2016. “I was inspired to build my own cluster so that I could gain more experience with a distributed system,” Rigoberto tells us. “I also needed a plan for my senior research project.” A cluster of Pis Rigoberto was able to submit this idea as his senior research project, and began researching. “The biggest challenge was to find a suitable, inexpensive computer that I could use as the nodes for my cluster. I originally thought about purchasing old desktops and chaining them up together to form a Beowulf cluster, but I wanted my cluster to be portable. The best choice was to use Raspberry Pis.” The cluster consists of a modest four Raspberry Pi 3s networked together. There’s also a cooling fan connected to the makeshift chassis for the setup, and extra heatsinks on the CPUs to help keep the system well ventilated. “My initial thought was that the CPU on board the Raspberry Pi would be the biggest limiting factor to obtaining the best performance out of the cluster,” Rigoberto says of his experiment, “but I had high hopes for my cluster.” The cluster was quite simple, using just four Raspberry Pis Benchmarking a cluster Rigoberto performed two major tests on his cluster, a Matrix Multiplication and an HPL (High-Performance LINPACK) benchmark. The Matrix Multiplication benchmark involves taking two matrices of the same size and multiplying them. The benchmark works by creating two matrices of random numbers of a given size. MPI (Message Passing Interface) is used to distribute evenly sized chunks of the matrices via Ethernet to every node/process, so that they work their chunk in…
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