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We implemented fuzzing swarms by parallel execution of multiple individual fuzzers which are clustered and relocated according to the algorithm described in Section 6.
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Parallelizations are based on the concept of modular parallelism, i.e. parallel execution of network modules.
However, using atomicAdd instructions to access the global memory is particularly slow, both because global memory access is a few orders of magnitude slower than that of on-chip memories and because atomicity prevents parallel execution of the code (by stalling other threads in the code segment where atomic instructions are located).
TM features can be leveraged to provide support for speculative parallel execution of irregular applications, characterized by a lack of knowledge about data dependences at compile-time.
However, the parallel execution of instructions is ultimately limited by true data dependencies between individual instructions.
The sdfMultiplexer program allows for the parallel execution of workflows over multiple cores or compute nodes by seamlessly splitting the input stream and combining the output streams.
Techniques for the partitioning of the image information, parallel execution of the queries, and strategies for workload balancing are explained by considering the parallel image database Cairo as an example.
This architecture allows a parallel execution of MATLAB requests.
In this paper we present several basic techniques for achieving parallel execution of constraint networks.
At the execution framework, we focus on support for many-core scheduling for parallel execution of concurrent programs.
These solutions are programming language specific and provide no solution for the parallel execution of tasks.
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