2,990 research outputs found
Designing a CPU model: from a pseudo-formal document to fast code
For validating low level embedded software, engineers use simulators that
take the real binary as input. Like the real hardware, these full-system
simulators are organized as a set of components. The main component is the CPU
simulator (ISS), because it is the usual bottleneck for the simulation speed,
and its development is a long and repetitive task. Previous work showed that an
ISS can be generated from an Architecture Description Language (ADL). In the
work reported in this paper, we generate a CPU simulator directly from the
pseudo-formal descriptions of the reference manual. For each instruction, we
extract the information describing its behavior, its binary encoding, and its
assembly syntax. Next, after automatically applying many optimizations on the
extracted information, we generate a SystemC/TLM ISS. We also generate tests
for the decoder and a formal specification in Coq. Experiments show that the
generated ISS is as fast and stable as our previous hand-written ISS.Comment: 3rd Workshop on: Rapid Simulation and Performance Evaluation: Methods
and Tools (2011
Performance Debugging and Tuning using an Instruction-Set Simulator
Instruction-set simulators allow programmers a detailed level of insight into,
and control over, the execution of a program, including parallel programs and
operating systems. In principle, instruction set simulation can model any
target computer and gather any statistic. Furthermore, such simulators are
usually portable, independent of compiler tools, and deterministic-allowing
bugs to be recreated or measurements repeated. Though often viewed as being
too slow for use as a general programming tool, in the last several years
their performance has improved considerably.
We describe SIMICS, an instruction set simulator of SPARC-based
multiprocessors developed at SICS, in its rĂ´le as a general programming tool.
We discuss some of the benefits of using a tool such as SIMICS to support
various tasks in software engineering, including debugging, testing, analysis,
and performance tuning. We present in some detail two test cases, where we've
used SimICS to support analysis and performance tuning of two applications,
Penny and EQNTOTT. This work resulted in improved parallelism in, and
understanding of, Penny, as well as a performance improvement for EQNTOTT of
over a magnitude. We also present some early work on analyzing SPARC/Linux,
demonstrating the ability of tools like SimICS to analyze operating systems
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