Resources

• Compute Simulation Infrastructure

  • SST : The Structural Simulation Toolkit (SST) was developed to explore innovations in highly concurrent systems where the ISA, microarchitecture, and memory interact with the programming model and communications system.
    • Webpage: http://sst-simulator.org/
  • Sparta: Sparta is a toolkit for modeling/simulation engineers to build CPU, GPU, and platform simulations.
    • Webpage: https://github.com/sparcians/map/tree/master/sparta
  • Accelergy : This is a energy/area estimation methodology for accelerators that allows design specifications comprised of user-defined high-level compound components and user-defined low-level primitive components, which can be characterized by external tools/plugins.
    • Webpage: https://accelergy.mit.edu/
  • Flamegraph : Tool to visualize perf events
    • Webpage: https://github.com/brendangregg/FlameGraph
  • Trace Viewer : This trace viewer from Chromium uses json based trace logging for visualization. It has a relatively simple interface and a well documented trace format.
    • Webpage: https://chromium.googlesource.com/external/github.com/catapult-project/catapult/+/refs/heads/master/tracing/docs/getting-started.md
  • Microsoft NNI: This framework can be used for sytemetically performing Design Space Exploration (with relatively low efforts), even though it is originally intended for neural network exploration.
    • Webpage: https://github.com/microsoft/nni

• Command line programs

  • ABC : ABC is a popular tool in the design automation community. It provides a comphrensive command line system for logic synthesis as well as verification.
    • Webpage: https://people.eecs.berkeley.edu/~alanmi/abc/
  • trash-cli : This is an alternative to rm tool for deleting files on the linux file system. Deleted files are cached and can be retrieved if possible.
    • Webpage: https://github.com/andreafrancia/trash-cli

• Neural Network tools

  • Netron : Netron is a viewer for neural network and capable of visualizing neural network models generated by a variety of deep learning frameworks, including ONNX, Caffe2, MXNet, etc.
    • Webpage : https://github.com/lutzroeder/netron

• Python libraries

  • Igraph : Igraph is a graph library that has multiple bindings available (including python, R, etc.) The library is well documented and can be used without a steep learning curve.
    • Webpage : https://igraph.org/python/doc/tutorial/tutorial.html
  • z3-solver: Z3 is a theorem prover from Microsoft Research with support for bitvectors, booleans, arrays, floating point numbers, strings, and other data types. This is a great tool for solving SAT and other optimization problems.
    • Webpage : https://pypi.org/project/z3-solver/
  • Dash : This can be used to build interactive graphs and other visualization from statistics generated by architectural simulators.
    • Webpage: https://dash.plotly.com/