  Computational modeling and simulation have become essential tools in the
quest to better understand the brain's makeup and to decipher the causal
interrelations of its components. The breadth of biochemical and biophysical
processes and structures in the brain has led to the development of a large
variety of model abstractions and specialized tools, often times requiring high
performance computing resource for their timely execution. What has been
missing so far was an in-depth analysis of the complexity of the computational
kernels, hindering a systematic approach to identifying bottlenecks of
algorithms and hardware, and their combinations. If whole brain models are to
be achieved on emerging computer generations, models and simulation engines
will have to be carefully co-designed for the intrinsic hardware tradeoffs. For
the first time, we present a systematic exploration based on analytic
performance modeling. We base our analysis on three in silico models, chosen as
representative examples of the most widely employed modeling abstractions. We
identify that the synaptic formalism, i.e. current or conductance based
representations, and not the level of morphological detail, is the most
significant factor in determining the properties of memory bandwidth saturation
and shared-memory scaling of in silico models. Even though general purpose
computing has, until now, largely been able to deliver high performance, we
find that for all types of abstractions, network latency and memory bandwidth
will become severe bottlenecks as the number of neurons to be simulated grows.
By adapting and extending a performance modeling approach, we deliver a first
characterization of the performance landscape of brain tissue simulations,
allowing us to pinpoint current bottlenecks in state-of-the-art in silico
models, and make projections for future hardware and software requirements.
