gem5-Aladdin SoC Simulator

harvard-acc

Welcome to the gem5-Aladdin SoC simulator!

This is a tool for end-to-end simulation of SoC workloads, including workloads with accelerated functions handled by fixed-function hardware blocks. With gem5-Aladdin, users can study the complex behaviors and interactions between general-purpose CPUs and hardware accelerators, including but not limited to cache coherency and memory consistency in heterogeneous platforms, data movement and communication, and shared resource contention, and how all these system-level effects impact overall application performance and speedup.

If you use gem5-Aladdin in your research, we would appreciate a citation to:

Co-Designing Accelerators and SoC Interfaces using gem5-Aladdin. Yakun Sophia Shao, Sam (Likun) Xi, Vijayalakashmi Srinvisan, Gu-Yeon Wei, and David Brooks. International Symposium on Microarchitecture (MICRO), June 2016. PDF

If you have any questions, please send them to the gem5-aladdin users mailing list (see link at the very bottom).

Table of Contents
  1. Notices
  2. Requirements
  3. Installation
  4. Running the simulator
  5. Writing a new program

Notices

Feburary 1st, 2020

Major update for gem5-Aladdin to version 2.0:

A new docker image has been pushed with environment updates for all of these changes. It is tagged llvm-6.0. Download here.

Usage of a few new features:

As an example shown below, now we can trace a C++ program, as long as the traced function is written in pure C with C-style linkage (the top_level function in the example is in an extern C context to ensure C-style linkage).

// Though we can write C++ code, only code with external C-style linkage will be
// instrumented (extern "C").
#ifdef __cplusplus
extern "C" {
#endif
int top_level(int a, int b) { return a + b; }
#ifdef __cplusplus
}
#endif

class Adder {
 public:
  Adder(int _a, int _b) : a(_a), b(_b) {}
  int run() {
    // The traced function needs to be pure C.
    return top_level(a, b);
  }

 private:
  int a;
  int b;
};

int main() {
  Adder adder(2, 3);
  int result = adder.run();
  std::cout << "result: " << result << "\n";
  return 0;
}

Some workloads are highly compute and memory intensive, such that simulating the complete workload would be infeasible because of trace storage limitations and simulation time. To make it possible to simulate bigger workloads, we introduce sampling support, which works at the loop granularity. The example below shows how to use the sampling API. The user informs Aladdin the sampled loop label and the sampling factor via a call to setSamplingFactor. Aladdin will unsample the sampled loop iterations and produce a final overall cycles estimate.

int reduction(int* a, int size, int sample) {
    // The DMA load is an operation that happens outside of the loop so it
    // doesn't need to be sampled.
    dmaLoad(a, size * sizeof(int));
    int result = 0;
    setSamplingFactor("loop", (float)size / sample);
    loop:
    // Run only `sample` iterations of this loop; the result
    // might be wrong, but that's expected for sampling.
    for (int i = 0; i < sample; i++)
        result += a[i]; return result;
}

We added a cycle-level model for a 2D systolic array accelerator, which supports computing convolution and matrix multiplication. Check out the usage in src/systolic_array/test.

This eliminates the polling involved in the CPUs while waiting for the accelerators to finish the assigned work. Instead, it puts the CPU to sleep using a magic simulator instruction. The accelerator will wake the CPU when it is done. This should also speed up simulation time by eliminating all the redundant memory operations required by polling. Please see src/aladdin/integration-test/with-cpu/test_multiple_accelerators as an example.

This is another choice for accelerator SoC interface, other than using software-managed DMAs and fully-coherent caches. It enables the accelerator to directly access the coherent data in the last-level-cache (LLC) without having a private cache. For more details and usage, please see the test_acp of the integration tests.

We also have a way to dynamically change the configured memory type of an array, using the setMemoryType API (see the integration test test_host_load_store).

December 1st, 2017

All commits from gem5 upstream as of 01/24/19 have been merged into gem5-Aladdin. Notable changes:

December 1st, 2017

gem5-Aladdin now has a Docker image! This image has all of gem5-aladdin's dependencies installed, and it comes with a basic set of development tools (e.g. vim). If you are having issues building the simulator because of dependency problems, please consider using Docker! The prebuilt image is located on Docker Hub.

See the docker directory for more details.

August 28th, 2017

All commits from gem5 upstream as of 8/17/17 have been merged into gem5-aladdin. Notable changes:

June 3rd, 2017

This branch has been renamed from devel to master and is now the default branch of this repository.

March 7th, 2017

This branch of gem5-Aladdin is based on gem5's development branch. The original release of gem5-Aladdin (still accessible via this repository's stable-old branch) was based on gem5's stable branch, which has been deprecated. The development branch and the stable branch have entirely separate histories. If you are a current user and you want to stay up to date with gem5-Aladdin, you must check out a completely fresh branch. You cannot simply merge the old branch with this new one!

We recommend that you clone a new local repository from this branch, rather than trying to bring this into your current local repository. To do so:

git clone -b devel https://github.com/harvard-acc/gem5-aladdin

The devel branch will soon be made the default branch, at which point you can drop the -b devel argument.

Requirements:

To build gem5-Aladdin, you will need to satisfy the dependencies of three projects: gem5, Aladdin, and Xenon.

gem5 dependencies

The main website can be found at http://www.gem5.org

A good starting point is http://www.gem5.org/Introduction, and for more information about building the simulator and getting started please see http://www.gem5.org/Documentation and http://www.gem5.org/Tutorials.

To build gem5, you will need the following software: g++ or clang, Python (gem5 links in the Python interpreter), SCons, ~~SWIG~~, zlib, m4, and lastly protobuf if you want trace capture and playback support. Please see http://www.gem5.org/Dependencies for more details concerning the minimum versions of the aforementioned tools.

If you want gem5 to dump stats in SQLite databases for easy access, you will also need to install SQLite3 development headers and libraries.

Aladdin dependencies

The main Aladdin repository is here. Users are recommended to see Aladdin's README for detailed instructions on installing dependencies.

In short, Aladdin's dependencies are:

  1. Boost Graph Library 1.55.0+
  2. GCC 4.8.1 or newer (we use C++11 features).
  3. LLVM 3.4 and Clang 3.4, 64-bit
  4. LLVM-Tracer (link).

Xenon dependencies

Xenon, the system we use for generating design sweep configurations, can be found here.

Xenon requires:

  1. Python 2.7.6+
  2. The pyparsing module (any version between 2.2.0 and 2.3.0, inclusive).

Installation

NOTE: If you are using the Docker image, please remember to update your local Git repository to the latest HEAD. The Docker image is only designed to set up your environment; it is your responsibility to ensure the code you're working off of is up to date. You can update just by running git pull origin master.

Setting up the source code

  1. If this is your first time, you will need to clone the repository.
git clone https://github.com/harvard-acc/gem5-aladdin
  1. Set up the Aladdin and Xenon submodules.
git submodule update --init --recursive

Building gem5-Aladdin

gem5 supports multiple architectures, but gem5-Aladdin currently only supports x86. ARM support is planned for a future release.

Type the following command to build the simulator:

scons build/X86/gem5.opt

This will build an optimized version of the gem5 binary (gem5.opt) for the specified architecture. You do not need to build Aladdin separately, unless you want to run Aladdin on its own. You can also replace gem5.opt with gem5.debug to build a binary suitable for use with a debugger. See http://www.gem5.org/Build_System for more details and options.

The basic source release includes these subdirectories:

Running gem5-Aladdin

gem5-Aladdin can be run in two ways: standalone and CPU.

In the standalone mode, there is no CPU in the system. gem5-Aladdin will simply invoke Aladdin, but now you get access to the complete gem5 memory system (where Aladdin alone supports private scratchpad memory only). In CPU mode, gem5-Aladdin will execute a user-level binary, which may invoke an accelerator after setting the necessary input data. gem5-Aladdin uses the num-cpus command-line parameter to distinguish between these two modes.

We have multiple integration tests that users can use as a starting point for running the simulator. They are located in gem5-aladdin/src/aladdin/integration-test, with both standalone and with-cpu options. To run any integration test, simply change into the appropriate directory and execute the following command:

sh run.sh

If successful, the output of the simulator will be placed under the outputs subdirectory, while the stdout dump will be preserved in stdout.gz.

Writing an accelerated program

For an example of how to write a program that invokes an Aladdin accelerator, we recommend starting with the integration tests test_load_store (which uses caches only) and test_dma_load_store (which uses DMA only). Both of these tests prepare data on the CPU, transfer the data into the accelerator, and expect the accelerator to modify the data in a particular way and write it into the memory system.

gem5-Aladdin does not currently support full-system simulation.

Questions

We would appreciate if you post any questions to the gem5-aladdin users mailing list.

gem5-aladdin users mailing list