SimRisk: An Integrated Open-Source Tool for Agent-Based ...

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Figure 5: A cluster of quad-core servers.
a hardware architecture and an agent-based model, how to distribute threads and processes
to cores and processors for better performance. This issue has a special meaning in a cluster
environment: a multi-core cluster supports both shared-memory and message-passing communication,
which have very different characteristics. We will study optimal distribution
of threads and processes for minimizing communication overhead in context of agent-based
supply-chain simulation. Specifically we will study the following methods:
(a) Explore model structure and data dependency to improve load balancing. For example,
consider the supply chain in Figure 1.(b), and assume that we will run simulation on
a cluster of quad-core processors, whose architecture is shown in Figure 5. Figure 6
shows a distribution of threads and processes using heuristics from model structure
and data dependency. The communication between supply-chain elements is through
shipments and messages. We assume that messages can only be passed along routes.
As a general principle, threads for a sub-network of closely coupled elements will be
placed on the cores of the same processor. These closely coupled elements require more
frequent communication between them, which shall be implemented with less overhead
using shared memory. As an example, in Figure 6 threads for the elements of the
sub-networks of w21 a and wb 21 are assigned to the same processor, and processes for the
sub-networks of s a and s b are allocated to different processors. In general, the higher
elements are in a network hierarchy, the less they will communicate with each other, since
the operations of elements on a higher level will be planned over a much bigger planning
horizon. We reserve shared memory for communication among closely coupled low-level
elements and use message passing for communication among high-level elements.
(b) Profile threads and optimize thread scheduling. To further improve the performance
of generated parallel simulators, we will profile the execution time and the overhead of
threads, and use this information to optimize thread scheduling. Using the result from
profiling, the generative simulation engine will express the thread scheduling problem as
a linear programming problem. It will use the optimization result to define scheduling
policy for threads.
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