Parallel code-specific CPU simulation with dynamic phase convergence modeling for HW/SW co-design

Kemmerer, Warren Hargon

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https://hdl.handle.net/2142/90847 Copy

Description

Title

Parallel code-specific CPU simulation with dynamic phase convergence modeling for HW/SW co-design

Author(s)

Kemmerer, Warren Hargon

Issue Date

2016-04-28

Director of Research (if dissertation) or Advisor (if thesis)

Chen, Deming

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Simulation
• Central processing unit (CPU)
• System on a chip (SoC)
• Phase
• Convergence
• SystemC

Abstract

While SystemC models provide a promising solution to the complex problem of HW/SW co-design within the system-on-chip paradigm, such requires a detailed annotation of transaction level energy and performance data within the model. While this data can be obtained through source code profiling of an application running on the target processor, accomplishing such when the target CPU hardware is not actively available typically requires time-consuming CPU simulation, which is often too slow to practically consider for large programs. Additionally, while the use of SystemC modeling with TLM 2.0 standard is widely adopted for the SoC modeling, the process of transforming C/C++ code to SystemC code with TLM 2.0 functionality remains non-trivial. Herein we propose an automated framework that: 1. Enables high speed code-specific CPU profiling support for both Sniper and gem5 using parallelized dynamic steady state phase convergence modeling, providing automatic annotation of energy and performance within source code. 2. Provides an automated C to SystemC TLM 2.0 code generation flow that utilizes the back-annotated source code to produce a SystemC module for seamless incorporation into the virtual prototype. Maximum speedups obtained using Sniper and gem5 are 48.76x and 562x respectively, while average results obtained speedups of 31.5x and 323.1x. Sniper results maintain an average accuracy of 0.89% for latency and 0.10% for energy, while gem5 achieves average accuracies of 4.16% and 2.87% for latency and energy respectively.

Graduation Semester

2016-05

Type of Resource

text

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http://hdl.handle.net/2142/90847

Copyright and License Information

Copyright 2016 Warren Hargon Kemmerer

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