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Advancing Compiler and Simulator Techniques for Highly Parallel Simulation of Embedded Systems

  • Author(s): Cheng, Zhongqi
  • Advisor(s): Doemer, Rainer
  • et al.

As an Electronic System Level (ESL) design language, the IEEE SystemC standard is widely used for testing, validation and verification of embedded system models. Discrete Event Simulation (DES) has been used for decades as the default SystemC simulation semantic. However, due to the sequential nature of DES, Parallel DES has recently gained an increasing amount of attention for performing high speed simulations on parallel computing platforms. To further exploit the parallel computation power of modern multi- and many-core platforms, Out-of-order Parallel Discrete Event Simulation (OoO PDES) has been proposed. In OoO PDES, threads comply with a partial order such that different simulation threads may run in different time cycles to increase the parallelism of execution. The Recoding Infrastructure for SystemC (RISC) has been introduced as a tool flow to fully support OoO PDES.

To preserve the SystemC semantics under OoO PDES, a compiler based approach statically analyzes the race conditions in the input model. However, there are severe restrictions: the source code for the input design must be available in one file, which does not scale. This disables the use of Intellectual Property (IP) and hierarchical file structures. In this dissertation, we propose a partial-graph based approach to scale the static analysis to support separate files and IP reuse. Specifically, the Partial Segment Graph (PSG) data structure is proposed and is used to abstract the behaviours and communication of modules within a single translation unit. These partial graphs are combined at top level to reconstruct the complete behaviors and communication of the entire model.

We also propose new algorithms to support the static analysis for modern SystemC TLM-2.0 standard. SystemC TLM-2.0 is widely used in industrial ESL designs for better interoperability and higher simulation speed. However, it is identified as an obstacle for parallel SystemC simulation due to the disappearance of channels. To solve the problem, we propose a compile time approach to statically analyze potential conflicts among threads in SystemC TLM-2.0 loosely- and approximately-timed models. A new Socket Call Path (SCP) technique is introduced which provides the compiler with socket binding information for precise static analysis. Based on SCP, an algorithm is proposed to analyze entangled variable pairs for automatic and accurate conflict analysis.

Besides the works on the compiler side, we focus as well on increasing the simulation speed of OoO PDES. We observe that the granularity of the Segment Graph (SG) data structure used in static analysis has a high impact on OoO PDES. This motivates us to propose a set of coding guidelines for the RISC users to properly refine their SystemC model for a higher simulation speed. Furthermore, in this dissertation, an algorithm is proposed to optimize directly the event delivery strategy in OoO PDES. Event delivery in OoO PDES was very conservative, which often postponed the execution of waiting threads due to unknown future behaviors of the SystemC model, and in turn became a bottleneck of simulation speed. The algorithm we propose takes advantage of the prediction of future thread behaviors, and therefore allows waiting threads to resume execution earlier, resulting in significantly increased simulation speed.

To summarize, the contributions of this dissertation include: 1) a scalable RISC tool flow for statically analyzing and protecting 3rd party IPs in models with multiple files, 2) an advanced static analysis approach for modern SystemC TLM-2.0 models, 3) a set of coding guidelines for RISC users to achieve higher simulation speed, and 4) a more efficient event delivery algorithm in OoO PDES scheduler using prediction information.

Together, these compiler and simulator advances enable OoO PDES for larger and modern model simulation and thus improve the design of embedded systems significantly, leading to better devices at lower cost in the end.

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