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Dynamic optimization has been proposed to overcome many limitations caused by static optimization and is widely applied in dynamic binary translation (DBT) to effectively enhance system performance. However, almost all the existing dynamic optimization techniques or methods employed in DBT systems for a single-threaded executive environment considerably increase the complexity of the hardware or the striking runtime overhead. We propose a multithreaded DBT framework with no associated hardwaredoi:10.1007/s11432-011-4414-5 fatcat:yaazmmdpfbbcbanlh5wh7hxokm