[pypy-commit] extradoc extradoc: blog post
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+===============================================================================
+Pydgin: Using RPython to Generate Fast Instruction-Set Simulators
+===============================================================================
+
+**Note:** This is a guest blog post by Derek Lockhart and Berkin Ilbeyi from
+Computer Systems Laboratory of Cornell University.
+
+In this blog post I'd like to describe some recent work on using the RPython
+translation toolchain to generate fast instruction set simulators.
+Our open-source framework, Pydgin [a]_, provides a domain-specific
+language (DSL) embedded in Python for concisely describing instruction set
+architectures [b]_ and then uses these descriptions to generate fast,
+JIT-enabled simulators.
+Pydgin will be presented at the *IEEE International Symposium on Performance
+Analysis of Systems and Software (ISPASS)* and in this post we provide a
+preview of that work.
+In addition, we discuss some additional progress updates that occurred after
+the publishing deadline and will not appear in the final paper [1]_.
+
+Our area of research expertise is computer architecture, which is perhaps an
+unfamiliar topic for some readers of the PyPy blog.
+Below we provide some brief background on hardware simulation in the field of
+computer architecture, as well as some context as to why instruction set
+simulators in particular are such an important tool.
+
+-------------------------------------------------------------------------------
+Simulators: Designing Hardware with Software
+-------------------------------------------------------------------------------
+
+For computer architects in both academia and industry, a key step in designing
+new computational hardware (e.g., CPUs, GPUs, and mobile system-on-chips) is
+simulation [c]_ of the target system.
+While numerous models for simulation exist, three classes are particularly
+important in hardware design.
+
+**Functional Level** models simulate the *behavior* of the target system.
+These models are useful for creating a "golden" reference which can serve as an
+executable specification or alternatively as an emulation platform for software
+development.
+
+**Cycle Level** models aim to simulate both the *behavior* and the approximate
+*timing* of a hardware component.
+These models help computer architects explore design tradeoffs and quickly
+determine things like how big caches should be, how many functional units are
+needed to meet throughput targets, and how the addition of a custom accelerator
+block may impact total system performance.
+
+**Register-Transfer Level** (RTL) models specify the *behavior*, *timing*, and
+*resources* (e.g., registers, wires, logic gates) of a hardware component.
+RTL models are bit-accurate hardware specifications typically written in a
+hardware description language (HDL) such as Verilog or VHDL.
+Once verified through extensive simulation, HDL specifications can be passed
+into synthesis and place-and-route tools to estimate area/energy/timing or to
+create FPGA or ASIC prototypes.
+
+An *instruction set simulator* (ISS) is a special kind of
+*functional-level* model that simulates the behavior of a processor or
+system-on-chip (SOC). ISSs serve an important role in hardware design
+because they model the instruction set architecture (ISA) interface: the
+contractual boundary between hardware designers and software developers.
+ISSs allow hardware designers to quickly experiment with adding new processor
+instructions while also allowing software developers to build new compilers,
+libraries, and applications long before physical silicon is available.
+
+-------------------------------------------------------------------------------
+Instruction-Set Simulators Must be Fast and Productive
+-------------------------------------------------------------------------------
+
+Instruction-set simulators are more important than ever because the ISA
+boundary has become increasingly fluid.
+While `Moore's law`_ has continued to deliver larger numbers of transistors
+which computer architects can use to build increasingly complex chips, limits
+in `Dennard scaling`_ have restricted how these transistors can be used [d]_.
+In more simple terms, thermal constraints (and energy constraints in mobile
+devices) have resulted in a growing interest in pervasive *specialization*:
+using custom accelerators to more efficiently perform compute intensive tasks.
+This is already a reality for designers of mobile SOCs who continually add new
+accelerator blocks and custom processor instructions in order to achieve higher
+performance with less energy consumption.
+ISSs are indispensable tools in this SOC design process for both hardware
+architects building the silicon and software engineers developing the software
+stack on top of it.
+
+An instruction set simulator has two primary responsibilities: 1) accurately
+emulating the external execution behavior of the target, and 2) providing
+observability by accurately reproducing the target's internal state (e.g.,
+register values, program counter, status flags) at each time step.
+However, other qualities critical to an effective ISS are **simulation
+performance** and **designer productivity**.
+Simulation performance is important because shorter simulation times allow
+developers to more quickly execute and verify large software applications.
+Designer productivity is important because it allows hardware architects to
+easily experiment with adding new instructions and estimate their impact on
+application performance.
+
+To improve simulation performance, high-performance ISSs use dynamic binary
+translation (DBT) as a mechanism to translate frequently visited blocks of
+target instructions into optimized sequences of host instructions.
+To improve designer productivity, many design toolchains automatically generate
+ISSs from an architectural description language (ADL): a special
+domain-specific language for succinctly specifying instruction encodings and
+instruction semantics of an ISA.
+Very few existing systems have managed to encapsulate the design complexity of
+DBT engines such that high-performance, DBT-accelerated ISSs could be
+automatically generated from ADLs [e]_.
+Unfortunately, tools which have done so are either proprietary software or
+leave much to be desired in terms of performance or productivity.
+
+-------------------------------------------------------------------------------
+Why RPython?
+-------------------------------------------------------------------------------
+
+Our research group learned of the RPython translation toolchain through our
+experiences with PyPy, which we had used in conjunction with our Python
+hardware modeling framework to achieve significant improvements in simulation
+performance [2]_.
+We realized that the RPython translation toolchain could potentially be adapted
+to create fast instruction set simulators since the process of interpreting
+executables comprised of binary instructions shared many similarities with the
+process of interpreting bytecodes in a dynamic-language VM.
+In addition, we were inspired by PyPy's meta-tracing approach to JIT-optimizing
+VM design which effectively separates the process of specifying a language
+interpreter from the optimization machinery needed to achieve good performance.
+
+Existing ADL-driven ISS generators have tended to use domain-specific
+languages that require custom parsers or verbose C-based syntax that
+distracts from the instruction specification.
+Creating an embedded-ADL within Python provides several benefits over these
+existing approaches including a gentler learning curve for new users, access to
+better debugging tools, and easier maintenance and extension by avoiding a
+custom parser.
+Additionally, we have found that the ability to directly execute Pydgin
+ISA descriptions in a standard Python interpreter such as CPython or PyPy
+significantly helps debugging and testing during initial ISA exploration.
+Python's concise, pseudocode-like syntax also manages to map quite closely to
+the pseudocode specifications provided by many ISA manuals [f]_.
+
+-------------------------------------------------------------------------------
+The Pydgin embedded-ADL
+-------------------------------------------------------------------------------
+
+Defining a new ISA in the Pydgin embedded-ADL requires four primary pieces of
+information: the architectural state (e.g. register file, program counter,
+control registers), the bit encodings of each instruction, the instruction
+fields, and the semantic definitions for each instruction. Pydgin aims to make
+this process as painless as possible by providing helper classes and functions
+where possible.
+
+For example, below we provide a truncated example of the ARMv5 instruction
+encoding table. Pydgin maintains encodings of all instructions in a centralized
+``encodings`` data structure for easy maintenance and quick lookup. The
+user-provided instruction names and bit encodings are used to automatically
+generate decoders for the simulator. Unlike many ADLs, Pydgin does not require
+that the user explicitly specify instruction types or mask bits for field
+matching because the Pydgin decoder generator can automatically infer decoder
+fields from the encoding table.
+
+.. code:: python
+
+ encodings = [
+ ['adc', 'xxxx00x0101xxxxxxxxxxxxxxxxxxxxx'],
+ ['add', 'xxxx00x0100xxxxxxxxxxxxxxxxxxxxx'],
+ ['and', 'xxxx00x0000xxxxxxxxxxxxxxxxxxxxx'],
+ ['b', 'xxxx1010xxxxxxxxxxxxxxxxxxxxxxxx'],
+ ['bl', 'xxxx1011xxxxxxxxxxxxxxxxxxxxxxxx'],
+ ['bic', 'xxxx00x1110xxxxxxxxxxxxxxxxxxxxx'],
+ ['bkpt', '111000010010xxxxxxxxxxxx0111xxxx'],
+ ['blx1', '1111101xxxxxxxxxxxxxxxxxxxxxxxxx'],
+ ['blx2', 'xxxx00010010xxxxxxxxxxxx0011xxxx'],
+ # ...
+ ['teq', 'xxxx00x10011xxxxxxxxxxxxxxxxxxxx'],
+ ['tst', 'xxxx00x10001xxxxxxxxxxxxxxxxxxxx'],
+ ]
+
+
+A major goal of Pydgin was ensuring instruction semantic definitions map to ISA
+manual specifications as much as possible. The code below shows one such
+definition for the ARMv5 ``add`` instruction.
+A user-defined ``Instruction`` class (not shown) specifies field names that can
+be used to conveniently access bit positions within an instruction (e.g.
+``rd``, ``rn``, ``S``).
+Additionally, users can choose to define their own helper functions, such as
+the ``condition_passed`` function, to create more concise syntax that better
+matches the ISA manual.
+
+.. code:: python
+
+ def execute_add( s, inst ):
+ if condition_passed( s, inst.cond() ):
+ a, = s.rf[ inst.rn() ]
+ b, _ = shifter_operand( s, inst )
+ result = a + b
+ s.rf[ inst.rd() ] = trim_32( result )
+
+ if inst.S():
+ if inst.rd() == 15:
+ raise FatalError('Writing SPSR not implemented!')
+ s.N = (result >> 31)&1
+ s.Z = trim_32( result ) == 0
+ s.C = carry_from( result )
+ s.V = overflow_from_add( a, b, result )
+
+ if inst.rd() == 15:
+ return
+
+ s.rf[PC] = s.fetch_pc() + 4
+
+Compared to the ARM ISA Reference manual shown below, the Pydgin instruction
+definition is a fairly close match. Pydgin's definitions could certainly be
+made more concise by using a custom DSL, however, this would lose many of the
+debugging benefits afforded to a well-supported language such as Python and
+additionally require using a custom parser that would likely need modification
+for each new ISA.
+
+.. code::
+
+ if ConditionPassed(cond) then
+ Rd = Rn + shifter_operand
+ if S == 1 and Rd == R15 then
+ if CurrentModeHasSPSR() then CPSR = SPSR
+ else UNPREDICTABLE else if S == 1 then
+ N Flag = Rd[31]
+ Z Flag = if Rd == 0 then 1 else 0
+ C Flag = CarryFrom(Rn + shifter_operand)
+ V Flag = OverflowFrom(Rn + shifter_operand)
+
+
+Creating an ISS that can run real applications is a rather complex task, even
+for a bare metal simulator with no operating system such as Pydgin.
+Each system call in the C library must be properly implemented, and
+bootstrapping code must be provided to set up the program stack and
+architectural state.
+This is a very tedious and error prone process which Pydgin tries to
+encapsulate so that it remains as transparent to the end user as possible.
+In future versions of Pydgin we hope to make bootstrapping more painless and
+support a wider variety of C libraries.
+
+.. Architectural state... leave out for now.
+.. ::
+
+ class State( object ):
+ _virtualizable_ = ['pc', 'ncycles']
+ def __init__( self, memory, debug, reset_addr=0x400 ):
+ self.pc = reset_addr
+ self.rf = ArmRegisterFile( self, num_regs=16 )
+ self.mem = memory
+
+ self.rf[ 15 ] = reset_addr
+
+ # current program status register (CPSR)
+ self.N = 0b0 # Negative condition
+ self.Z = 0b0 # Zero condition
+ self.C = 0b0 # Carry condition
+ self.V = 0b0 # Overflow condition
+
+ # other registers
+ self.status = 0
+ self.ncycles = 0
+
+ def fetch_pc( self ):
+ return self.pc
+
+
+-------------------------------------------------------------------------------
+Pydgin Performance
+-------------------------------------------------------------------------------
+
+In order to achieve good simulation performance from Pydgin ISSs, significant
+work went into adding appropriate JIT annotations to the Pydgin library
+components.
+These optimization hints, which allow the JIT generated by the RPython
+translation toolchain to produce more efficient code, have been specifically
+selected for the unique properties of ISSs.
+For the sake of brevity, we do not talk about the exact optimizations here but
+a detailed discussion can be found in the ISPASS paper [1]_.
+In the paper we evaluate two ISSs, one for a simplified MIPS ISA and another
+for the ARMv5 ISA, whereas below we only discuss results for the ARMv5 ISS.
+
+The performance of Pydgin-generated ARMv5 ISSs were compared against
+several reference ISSs: the gem5_ ARM atomic simulator (*gem5*),
+interpretive and JIT-enabled versions of SimIt-ARM_ (*simit-nojit* and
+*simit-jit*), and QEMU_.
+Atomic models from the gem5 simulator were chosen for comparison due their wide
+usage amongst computer architects [g]_.
+SimIt-ARM was selected because it is currently the highest performance
+ADL-generated DBT-ISS publicly available.
+QEMU has long been held as the gold-standard for DBT simulators due to its
+extremely high performance, however, QEMU is generally intended for usage as an
+emulator rather than a simulator [c]_ and therefore achieves its excellent
+performance at the cost of observability.
+Unlike QEMU, all other simulators in our study faithfully track architectural
+state at an instruction level rather than block level.
+Pydgin ISSs were generated with and without JITs using the RPython translation
+toolchain in order to help quantify the performance benefit of the meta-tracing
+JIT.
+
+The figure below shows the performance of each ISS executing applications from
+the SPEC CINT2006 benchmark suite [h]_.
+Benchmarks were run to completion on the high-performance DBT-ISSs
+(*simit-jit*, *pydgin-jit*, and QEMU), but were terminated after only
+10 billion simulated instructions for the non-JITed interpretive ISSs
+(these would require many hours, in some cases days, to run to completion).
+Simulation performance is measured in MIPS [i]_ and plotted on a **log
+scale** due to the wide variance in performance.
+The *WHMEAN* group summarizes each ISS's performance across all benchmarks
+using the weighted harmonic mean.
+
+.. image:: arm-bar-plot.png
+
+A few points to take away from these results:
+
+- ISSs without JITs (*gem5*, *simit-nojit*, and *pydgin-nojit*) demonstrate
+ relatively consistent performance across applications, whereas ISSs with JITs
+ (*simit-jit*, *pydgin-jit*, and QEMU) demonstrate much greater
+ performance variability from application-to-application.
+- The *gem5* atomic model demonstrates particularly miserable performance, only
+ 2-3 MIPS!
+- QEMU lives up to its reputation as a gold-standard for simulator performance,
+ leading the pack on nearly every benchmark and reaching speeds of 240-1120
+ MIPS.
+- *pydgin-jit* is able to outperform *simit-jit* on four of the
+ applications, including considerable performance improvements of 1.44–1.52×
+ for the applications *456.hmmer*, *462.libquantum*, and *471.omnetpp*
+ (managing to even outperform QEMU on *471.omnetpp*).
+- *simit-jit* is able to obtain much more consistent performance (230-459
+ MIPS across all applications) than *pydgin-jit* (9.6-659 MIPS). This is
+ due to *simit-jit*'s page-based approach to JIT optimization compared to
+ *pydgin-jit*'s tracing-based approach.
+- *464.h264ref* displays particularly bad pathological behavior in Pydgin’s
+ tracing JIT and is the only application to perform worse on *pydgin-jit*
+ than *pydgin-nojit* (9.6 MIPS vs. 21 MIPS).
+
+The pathological behavior demonstrated by *464.h264ref* was of particular
+concern because it caused *pydgin-jit* to perform even worse than having no
+JIT at all. RPython JIT logs indicated that the reason for this performance
+degradation was a large number of tracing aborts due to JIT traces growing too
+long. However, time limitations before the publication deadline prevented us
+from investigating this issue thoroughly.
+
+Since the deadline we've applied some minor bug fixes and made some small
+improvements in the memory representation.
+More importantly, we've addressed the performance degradation in *464.h264ref*
+by increasing trace lengths for the JIT.
+Below we show how the performance of *464.h264ref* changes as the
+**trace_limit** parameter exposed by the RPython JIT is varied from the default
+size of 6000 operations.
+
+.. image:: trace-length-plot.png
+
+By quadrupling the trace limit we achieve an 11x performance improvement in
+*464.h264ref*.
+The larger trace limit allows the JIT to optimize long code paths that were
+previously triggering trace aborts, greatly helping amortize the costs of
+tracing.
+Note that arbitrarily increasing this limit can potentially hurt performance if
+longer traces are not able to detect optimizable code sequences.
+
+After performing similar experiments across the applications in the SPEC
+CINT2006 benchmark suite, we settled on a trace limit of 400,000 operations.
+In the figure below we show how the updated Pydgin ISS (*pydgin-400K*) improves
+performance across all benchmarks and fixes the performance degradation
+previously seen in *464.h264ref*. Note that the non-JITted simulators have been
+removed for clarity, and simulation performance is now plotted on a
+**linear scale** to more clearly distinguish the performance gap between
+each ISS.
+
+.. image:: new-bar-plot.png
+
+With these improvements, we are now able to beat *simit-jit* on all but two
+benchmarks. In future work we hope to further close the gap with QEMU as well.
+
+-------------------------------------------------------------------------------
+Conclusions and Future Work
+-------------------------------------------------------------------------------
+
+Pydgin demonstrates that the impressive work put into the RPython translation
+toolchain, designed to simplify the process of building fast dynamic-language
+VMs, can also be leveraged to build fast instruction set simulators.
+Our prototype ARMv5 ISS shows that Pydgin can generate ISSs with performance
+competitive to SimIt-ARM while also providing a more productive development
+experience: RPython allowed us to develop Pydgin with only four person-months
+of work.
+Another significant benefit of the Pydgin approach is that any performance
+improvements applied to the RPython translation toolchain immediately benefit
+Pydgin ISSs after a simple software download and retranslation.
+This allows Pydgin to track the continual advances in JIT technology introduced
+by the PyPy development team.
+
+Pydgin is very much a work in progress. There are many features we would like
+to add, including:
+
+- more concise syntax for accessing arbitrary instruction bits
+- support for other C libraries such as glibc, uClibc, and musl
+ (we currently only support binaries compiled with newlib)
+- support for self-modifying code
+- features for more productive debugging of target applications
+- ISS descriptions for other ISAs such as RISC-V, ARMv8, and x86
+- automatic generation of compilers and toolchains from Pydgin descriptions
+
+In addition, we think there are opportunities for even greater performance
+improvements with more advanced techniques such as:
+
+- automatic generation of optimized instruction decoders
+- optimizations for floating-point intensive applications
+- multiple tracing-JITs for parallel simulation of multicore SOCs
+- a parallel JIT compilation engine as proposed by Böhm et al. [3]_
+
+We hope that Pydgin can be of use to others, so if you try it out please let us
+know what you think. Feel free to contact us if you find any of the above
+development projects interesting, or simply fork the project on GitHub and hack
+away!
+
+-- Derek Lockhart and Berkin Ilbeyi
+
+-------------------------------------------------------------------------------
+Acknowledgements
+-------------------------------------------------------------------------------
+
+We would like to sincerely thank Carl Friedrich Bolz and Maciej
+Fijalkowski for their feedback on the Pydgin publication and their
+guidance on improving the JIT performance of our simulators. We would also
+like to thank for the whole PyPy team for their incredible work on the
+PyPy and the RPython translation toolchain.
+
+-------------------------------------------------------------------------------
+Footnotes
+-------------------------------------------------------------------------------
+
+.. [a] Pydgin loosely stands for [Py]thon [D]SL for [G]enerating
+ [In]struction set simulators and is pronounced the same as “pigeon”. The
+ name is inspired by the word “pidgin” which is a grammatically simplified
+ form of language and captures the intent of the Pydgin embedded-ADL.
+ https://github.com/cornell-brg/pydgin
+.. [b] Popular instruction set architectures (ISAs) include MIPs, ARM,
+ x86, and more recently RISC-V
+.. [c] For a good discussion of simulators vs. emulators, please see the
+ following post on StackOverflow:
+ http://stackoverflow.com/questions/1584617/simulator-or-emulator-what-is-the-difference
+.. [d] http://en.wikipedia.org/wiki/Dark_silicon
+.. [e] Please see the Pydgin paper for a more detailed discussion of prior work.
+.. [f] For more examples of Pydgin ISA specifications, please see the ISPASS
+ paper [1]_ or the Pydgin source code on GitHub.
+
+ Pydgin instruction definitions for a simple MIPS-inspired ISA can be
+ found here:
+
+ - https://github.com/cornell-brg/pydgin/blob/master/parc/isa.py
+
+ Pydgin instruction definitions for a simplified ARMv5 ISA can be found
+ here:
+
+ - https://github.com/cornell-brg/pydgin/blob/master/arm/isa.py
+
+.. [g] gem5 is a cycle-level simulation framework that contains both
+ functional-level (atomic) and cycle-level processor models. Although
+ primarily used for detailed, cycle-approximate processor simulation,
+ gem5's atomic model is a popular tool for many ISS tasks.
+
+ - http://www.m5sim.org/SimpleCPU
+.. [h] All performance measurements were taken on an unloaded server-class
+ machine.
+.. [i] Millions of instructions per second.
+
+.. _gem5: http://www.gem5.org/
+.. _SimIt-ARM: http://simit-arm.sourceforge.net/
+.. _QEMU: http://wiki.qemu.org/
+.. _`Moore's Law`: http://en.wikipedia.org/wiki/Moore%27s_law
+.. _`Dennard scaling`: http://en.wikipedia.org/wiki/Dennard_scaling#Recent_breakdown_of_Dennard_scaling
+
+-------------------------------------------------------------------------------
+References
+-------------------------------------------------------------------------------
+
+.. [1] Derek Lockhart, Berkin Ilbeyi, and Christopher Batten. "Pydgin:
+ Generating Fast Instruction Set Simulators from Simple Architecture
+ Descriptions with Meta-Tracing JIT Compilers." IEEE Int'l Symp. on
+ Performance Analysis of Systems and Software (ISPASS), Mar. 2015.
+
+ - http://csl.cornell.edu/~cbatten/pdfs/lockhart-pydgin-ispass2015.pdf
+ - https://github.com/cornell-brg/pydgin
+
+.. [2] Derek Lockhart, Gary Zibrat, and Christopher Batten. "PyMTL: A Unified
+ Framework for Vertically Integrated Computer Architecture Research." 47th
+ ACM/IEEE Int'l Symp. on Microarchitecture (MICRO-47), Dec. 2014.
+
+ - http://csl.cornell.edu/~cbatten/pdfs/lockhart-pymtl-micro2014.pdf
+ - https://github.com/cornell-brg/pymtl
+
+.. [3] I. Böhm, B. Franke, and N. Topham. Generalized Just-In-Time Trace
+ Compilation Using a Parallel Task Farm in a Dynamic Binary Translator.
+ ACM SIGPLAN Conference on Programming Language Design and Implementation
+ (PLDI), Jun 2011.
+
+
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