42,288 research outputs found
A new class of highly efficient exact stochastic simulation algorithms for chemical reaction networks
We introduce an alternative formulation of the exact stochastic simulation
algorithm (SSA) for sampling trajectories of the chemical master equation for a
well-stirred system of coupled chemical reactions. Our formulation is based on
factored-out, partial reaction propensities. This novel exact SSA, called the
partial propensity direct method (PDM), is highly efficient and has a
computational cost that scales at most linearly with the number of chemical
species, irrespective of the degree of coupling of the reaction network. In
addition, we propose a sorting variant, SPDM, which is especially efficient for
multiscale reaction networks.Comment: 23 pages, 3 figures, 4 tables; accepted by J. Chem. Phy
Spark deployment and performance evaluation on the MareNostrum supercomputer
In this paper we present a framework to enable data-intensive Spark workloads on MareNostrum, a petascale supercomputer designed mainly for compute-intensive applications. As far as we know, this is the first attempt to investigate optimized deployment configurations of Spark on a petascale HPC setup. We detail the design of the framework and present some benchmark data to provide insights into the scalability of the system. We examine the impact of different configurations including parallelism, storage and networking alternatives, and we discuss several aspects in executing Big Data workloads on a computing system that is based on the compute-centric paradigm. Further, we derive conclusions aiming to pave the way towards systematic and optimized methodologies for fine-tuning data-intensive application on large clusters emphasizing on parallelism configurations.Peer ReviewedPostprint (author's final draft
Energy-recycling Blockchain with Proof-of-Deep-Learning
An enormous amount of energy is wasted in Proofof-Work (PoW) mechanisms
adopted by popular blockchain applications (e.g., PoW-based cryptocurrencies),
because miners must conduct a large amount of computation. Owing to this, one
serious rising concern is that the energy waste not only dilutes the value of
the blockchain but also hinders its further application. In this paper, we
propose a novel blockchain design that fully recycles the energy required for
facilitating and maintaining it, which is re-invested to the computation of
deep learning. We realize this by proposing Proof-of-Deep-Learning (PoDL) such
that a valid proof for a new block can be generated if and only if a proper
deep learning model is produced. We present a proof-of-concept design of PoDL
that is compatible with the majority of the cryptocurrencies that are based on
hash-based PoW mechanisms. Our benchmark and simulation results show that the
proposed design is feasible for various popular cryptocurrencies such as
Bitcoin, Bitcoin Cash, and Litecoin.Comment: 5 page
- …