M2.xlarge on ec2 pricing8/6/2023 ![]() Monte Carlo simulation is the gold standard for modeling complex physical systems, such as photon migration in biological tissue. In biomedical applications, the need for high-performance computing is growing as technology evolves toward more accurate imaging and treatment delivery methods. As a result, scientific computing is inexorably shifting to parallel architectures. Furthermore, multicore processors and many-core graphics processing units (GPUs) are now the industry standard for high-performance computing. However, in many applications, the growth of scientific data has outpaced the performance of single-core processors. ![]() Researchers have long relied on single-threaded programming for solving computational problems. The distributed simulation produced the same output as the original implementation and was resilient to hardware failure: the correctness of the simulation was unaffected by the shutdown of 50% of the nodes. The overall computational throughput was 85,178 photon histories per node per second, with a latency of 100 s. ![]() For a cluster size of 240 nodes, the simulation of 100 billion photon histories took 22 min, a 1258 × speed-up compared to the single-threaded Monte Carlo program. The simulation time was found to be linearly dependent on the number of photons and inversely proportional to the number of nodes. The distributed implementation was evaluated on a commercial compute cloud. In this implementation, Map tasks compute photon histories in parallel while a Reduce task scores photon absorption. We ported the MC321 Monte Carlo package to Hadoop, an open-source MapReduce framework. The purpose of this work is to report on our implementation of a simple MapReduce method for performing fault-tolerant Monte Carlo computations in a massively-parallel cloud computing environment. However, its widespread use is hindered by the high computational cost. Monte Carlo simulation is considered the most reliable method for modeling photon migration in heterogeneous media.
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