The Legate project endeavors to democratize computing by making it possible for all programmers to leverage the power of large clusters of CPUs and GPUs by running the same code that runs on a desktop or a laptop at scale. Using this technology, computational and data scientists can develop and test programs on moderately sized data sets on local machines and then immediately scale up to larger data sets deployed on many nodes in the cloud or on a supercomputer without any code modifications. In visual terms:


The Legate project is built upon two foundational principles:

  1. For end users, such as computational and data scientists, the programming model must be identical to programming a single sequential CPU on their laptop or desktop. All concerns relating to parallelism, data distribution, and synchronization must be implicit. The cloud or a supercomputer should appear as nothing more than a super-powerful CPU core.

  2. Software must be compositional and not just interoperable (i.e. functionally correct). Libraries developed in the Legate ecosystem must be able to exchange partitioned and distributed data without requiring “shuffles” or unnecessary blocking synchronization. Computations from different libraries should be able to use arbitrary data and still be reordered across abstraction boundaries to hide communication and synchronization latencies where the original sequential semantics of the program allow. This is essential for achieving speed-of-light performance on large scale machines.

The Legate project is still in its nascent stages of development, but much of the fundamental architecture is in place. We encourage development and contributions to existing Legate libraries, such as Legate NumPy and Legate Pandas, as well as the development of new Legate libraries. Pull requests are welcomed.

  1. Why Legate?

  2. What is the Legate Core?

  3. How Does Legate Work?

  4. How Do I Install Legate?

  5. How Do I Use Legate?

  6. Other FAQs

  7. Documentation

  8. Next Steps

Why Legate?

Computational problems today continue to grow both in their complexity as well as the scale of the data that they consume and generate. This is true both in traditional HPC domains as well as enterprise data analytics cases. Consequently, more and more users truly need the power of large clusters of both CPUs and GPUs to address their computational problems. Not everyone has the time or resources required to learn and deploy the advanced programming models and tools needed to target this class of hardware today. Legate aims to bridge this gap so that any programmer can run code on any scale machine without needing to be an expert in parallel programming and distributed systems, thereby allowing developers to bring the problem-solving power of large machines to bear on more kinds of challenging problems than ever before.

What is the Legate Core?

The Legate Core is our version of Apache Arrow. Apache Arrow has significantly improved composability of software libraries by making it possible for different libraries to share in-memory buffers of data without unnecessary copying. However, it falls short when it comes to meeting two of our primary requirements for Legate:

  1. Arrow only provides an API for describing a physical representation of data as a single memory allocation. There is no interface for describing cases where data has been partitioned and then capturing the logical relationships of those partitioned subsets of data.

  2. Arrow is mute on the subject of synchronization. Accelerators such as GPUs achieve significantly higher performance when computations are performed asynchronously with respect to other components of the system. When data is passed between libraries today, accelerators must be pessimistically synchronized to ensure that data dependences are satisfied across abstraction boundaries. This might result in tolerable overheads for single GPU systems, but can result in catastrophically poor performance when hundreds of GPUs are involved.

The Legate Core provides an API very similar to Arrow’s interface with several important distinctions that provide stronger guarantees about data coherence and synchronization to aid library developers when building Legate libraries. These guarantees are the crux of how libraries in the Legate ecosystem are able to provide excellent composability.

The Legate Core API imports several important concepts from Arrow such that users that are familiar with Arrow already will find it unsurprising. We use the same type system representation as Arrow so libraries that have already adopted it do not need to learn or adapt to a new type system. We also reuse the concept of an Array from Arrow. The LegateArray class supports many of the same methods as the Arrow Array interface (we’ll continue to add methods to improve compatibility). The main difference is that instead of obtaining Buffer objects from arrays to describe allocations of data that back the array, the Legate Core API introduces a new primitive called a LegateStore which provides a new interface for reasoning about partitioned and distributed data in asynchronous execution environments.

Any implementation of a LegateStore must maintain the following guarantees to clients of the Legate Core API (i.e. Legate library developers):

  1. The coherence of data contained in a LegateStore must be implicitly managed by the implementation of the Legate Core API. This means that no matter where data is requested to perform a computation in a machine, the most recent modifications to that data in program order must be reflected. It should never be clients responsibility to maintain this coherence.

  2. It should be possible to create arbitrary views onto LegateStore objects such that library developers can precisely describe the working sets of their computations. Modifications to views must be reflected onto all aliasing views data. This property must be maintained by the Legate Core API implementation such that it is never the concern of clients.

  3. Dependence management between uses of the LegateStore objects and their views is the responsibility of Legate Core API regardless of what (asynchronous) computations are performed on LegateStore objects or their views. This dependence analysis must be both sound and precise. It is illegal to over-approximate dependences. This dependence analysis must also be performed globally in scope. Any use of the LegateStore on any processor/node in the system must abide by the original sequential semantics of the program

Note that we do not specify exactly what the abstractions are that are needed for implementing LegateStore objects. Our goal is not prescribe what these abstractions are as they may be implementation dependent. Our only requirements are that they have these properties to ensure that incentives are aligned in such a way for Legate libraries to achieve a high degree of composability at any scale of machine. Indeed, these requirements shift many of the burdens that make implementing distributed and accelerated libraries hard off of the library developers and onto the implementation of the Legate Core API. This is by design as it allows the costs to be amortized across all libraries in the ecosystem and ensures that Legate library developers are more productive.

How Does Legate Work?

Our implementation of the Legate Core API is built on top of the Legion programming model and runtime system. Legion was originally designed for large HPC applications that target supercomputers and consequently applications written in the Legion programming model tend to both perform and scale well on large clusters of both CPUs and GPUs. Legion programs are also easy to port to new machines as they inherently decouple the machine-independent specification of computations from decisions about how that application is mapped to the target machine. Due to this abstract nature, many programmers find writing Legion programs challenging. By implementing the Legate Core API on top of Legion, we’ve made it easier to use Legion such that developers can still get access to the benefits of Legion without needing to learn all of the lowest-level interfaces.

The Legion programming model greatly aids in implementing the Legate Core API. Data types from libraries, such as arrays in Legate NumPy are mapped down onto LegateStore objects that wrap Legion data types such as logical regions or futures. In the case of regions, Legate application libraries rely heavily on Legion’s support for partitioning of logical regions into arbitrary subregion views. Each library has its own heuristics for computing such partitions that take into consideration the computations that will access the data, the ideal sizes of data to be consumed by different processor kinds, and the available number of processors. Legion automatically manages the coherence of subregion views regardless of the scale of the machine.

Computations in Legate application libraries are described by Legion tasks. Tasks describe their data usage in terms of LegateStore objects, thereby allowing Legion to infer where dependences exist. Legion uses distributed bounding volume hierarchies, similar to a high performance ray-tracer, to soundly and precisely perform dependence analysis on logical regions and insert the necessary synchronization between tasks to maintain the original sequential semantics of a Legate program.

Each Legate application library also comes with its own custom Legion mapper that uses heuristics to determine the best choice of mapping for tasks (e.g. are they best run on a CPU or a GPU). All Legate tasks are currently implemented in native C or CUDA in order to achieve excellent performance on the target processor kind, but Legion has bindings in other languages such as Python, Fortran, and Lua for users that would prefer to use them. Importantly, by using Legion, Legate is able to control the placement of data in order to leave it in-place in fast memories like GPU framebuffers across tasks.

When running on large clusters, Legate leverages a novel technology provided by Legion called “control replication” to avoid the sequential bottleneck of having one node farm out work to all the nodes in the cluster. With control replication, Legate will actually replicate the Legate program and run it across all the nodes of the machine at the same time. These copies of the program all cooperate logically to appear to execute as one program. When communication is necessary between different computations, the Legion runtime’s program analysis will automatically detect it and insert the necessary data movement and synchronization across nodes (or GPU framebuffers). This is the transformation that allows sequential programs to run efficiently at scale across large clusters as though they are running on a single processor.

How Do I Install Legate?


Legate has been tested on both Linux and Darwin operating systems, although only a few flavors of Linux such as Ubuntu have been thoroughly tested. There is currently no support for Windows.

The Legate Core currently requires Python >= 3.7 and the following packages:

  • pyarrow=1.0.1

  • numpy

  • CUDA >= 8.0

  • C++14 compatible compiler (g++, clang, or nvc++)


The Legate Core library comes with both a standard setup.py script and a custom install.py script in the top-level directory of the repository that will build and install the Legate Core library. Users can use either script to install Legate as they will produce the same effect. A simple single-node, CPU-only installation of Legate into targetdir will be performed by:

./setup.py --install-dir targetdir

To add GPU support for Legate simply use the --cuda flag. The first time you request GPU support you will need to use the --with-cuda flag to specify the location of your CUDA installation. For later invocations you do not need to use this flag again the installation scripts will remember the location of your CUDA installation until you tell it differently. You can also specify the name of the CUDA architecture you want to target with the --arch flag (the default is volta but you can also specify kepler, maxwell, pascal, or ampere).

./install.py --cuda --with-cuda /usr/local/cuda/ --arch volta

For multi-node support Legate uses GASNet which can be requested using the the --gasnet flag. If you have an existing GASNet installation then you can inform the install script with the --with-gasnet flag. The install.py script also requires you to specify the interconnect network of the target machine using the --conduit flag (current choices are one of ibv for Infiniband, or gemini or aries for Cray interconnects). For example this would be an installation for a DGX SuperPOD:

./install.py --gasnet --conduit ibv --cuda --arch ampere

Alternatively here is an install line for the Piz-Daint supercomputer:

./install.py --gasnet --conduit aries --cuda --arch pascal

To see all the options available for installing Legate, just run with the --help flag:

./install.py --help

Options passed to setup.py will automatically be forwarded to install.py so that users can use them interchangeably (this provides backwards compatibility for earlier versions of Legate when only install.py existed).

Python used by Legate

Legate discovers the Python library and version during build time, and then it builds all successive Legate libraries against that version of Python. The build system tries to detect the Python setup from the default Python interpreter, but sometimes it is unsuccessful or a different version of Python than the one in the environment may be desired. To use a different version of Python than the one available in the environment, the PYTHON_ROOT variable must be set to the base directory of the desired Python installation.

Sometimes, the search for the Python library may fail. In such situation, the build system generates a warning:

runtime.mk: cannot find libpython3.6*.so - falling back to using LD_LIBRARY_PATH

In this case, Legate will use the Python library that is available at runtime, if any. To explicitly specify the Python library to use, PYTHON_LIB should be set to the location of the library, and PYTHON_VERSION_MAJOR should be set to 3.

Toolchain selection

Legate relies on environment variables to select its toolchain and build flags (such as CXX, CC_FLAGS, LD_FLAGS, NVCC_FLAGS). Setting these environment variables prior to building and installing Legate will influence the build of any C++ and CUDA code in Legate.

How Do I Use Legate?

After installing the Legate Core library, the next step is to install a Legate application library such as Legate NumPy. The installation process for a Legate application library will require you to provide a pointer to the location of your Legate Core library installation as this will be used to configure the installation of the Legate application library. After you finish installing any Legate application libraries, you can then simply replace their import statements with the equivalent ones from any Legate application libraries you have installed. For example, you can change this:

import numpy as np

to this:

import legate.numpy as np

After this, you can use the legate driver script in the bin directory of your installation to run any Python program. For example, to run your script in the default configuration (4 CPUs cores and 4 GB of memory) just run:

installdir/bin/legate my_python_program.py [other args]

The legate script also allows you to control the amount of resources that Legate consumes when running on the machine. The --cpus and --gpus flags are used to specify how many CPU and GPU processors should be used on a node. The --sysmem flag can be used to specify how many MBs of DRAM Legate is allowed to use per node, while the --fbmem flag controls how many MBs of framebuffer memory Legate is allowed to use per GPU. For example, when running on a DGX station, you might run your application as follows:

installdir/bin/legate --cpus 16 --gpus 4 --sysmem 100000 -fbmem 15000 my_python_program.py

This will make 16 CPU processors and all 4 GPUs available for use by Legate. It will also allow Legate to consume up to 100 GB of DRAM memory and 15 GB of framebuffer memory per GPU for a total of 60 GB of GPU framebuffer memory. Note that you probably will not be able to make all the resources of the machine available for Legate as some will be used by the system or Legate itself for meta-work. Currently if you try to exceed these resources during execution then Legate will inform you that it had insufficient resources to complete the job given its current mapping heuristics. If you believe the job should fit within the assigned resources please let us know so we can improve our mapping heuristics. There are many other flags available for use in the legate driver script that you can use to communicate how Legate should view the available machine resources. You can see a list of them by running:

installdir/bin/legate --help

In addition to running Legate programs, you can also use Legate in an interactive mode by simply not passing any *py files on the command line. You can still request resources just as you would though with a normal file. Legate will still use all the resources available to it, including doing multi-node execution.

installdir/bin/legate --cpus 16 --gpus 4 --sysmem 100000 -fbmem 15000
Welcome to Legion Python interactive console

Note that Legate does not currently support multi-tenancy cases where different users are attempting to use the same hardware concurrently.

Distributed Launch

If legate is compiled with GASNet support (see the installation section), it can be run in parallel by using the --nodes option followed by the number of nodes to be used. Whenever the --nodes option is used, Legate will be launched using mpirun, even with --nodes 1. Without the --nodes option, no launcher will be used. Legate currently supports mpirun, srun, and jsrun as launchers and we are open to adding additional launcher kinds. You can select the target kind of launcher with --launcher.

Debugging and Profiling

Legate also comes with several tools that you can use to better understand your program both from a correctness and a performance standpoint. For correctness, Legate has facilities for constructing both dataflow and event graphs for the actual run of an application. These graphs require that you have an installation of GraphViz available on your machine. To generate a dataflow graph for your Legate program simply pass the --dataflow flag to the legate.py script and after your run is complete we will generate a dataflow_legate.pdf file containing the dataflow graph of your program. To generate a corresponding event graph you simply need to pass the --event flag to the legate.py script to generate a event_graph_legate.pdf file. These files can grow to be fairly large for non-trivial programs so we encourage you to keep your programs small when using these visualizations or invest in a robust PDF viewer.

For profiling all you need to do is pass the --profile flag to Legate and afterwards you will have a legate_prof directory containing a web page that can be viewed in any web browser that displays a timeline of your program’s execution. You simply need to load the index.html page from a browser. You may have to enable local JavaScript execution if you are viewing the page from your local machine (depending on your browser).

We recommend that you do not mix debugging and profiling in the same run as some of the logging for the debugging features requires significant file I/O that can adversely effect the performance of the application.

Other FAQs

  • Does Legate only work on NVIDIA hardware? No, Legate will run on most kinds of hardware, anywhere that Legion and GASNet will run. This includes x86, ARM, and PowerPC CPUs. GASNet (and therefore Legate) also includes support for Infiniband, Cray, Omnipath, and (ROC-)Ethernet based interconnects.

  • What languages does the Legate Core API have bindings for? Currently the Legate Core bindings are only available in Python. Watch this space for new language bindings soon or make a pull request to contribute your own. Legion has a C API which should make it easy to develop bindings in any language with a foreign function interface.

  • Do I have to build drop-in replacement libraries? No! While we’ve chosen to provide drop-in replacement libraries for popular Python libraries to illustrate the benefits of Legate, you are both welcomed and encouraged to develop your own libraries on top of Legate. We promise that they will compose well with other existing Legate libraries.

  • What other libraries are you planning to release for the Legate ecosystem? We’re still working on that. If you have thoughts about what is important please let us know so that we can get a feel for where best to put our time.

  • Can I use Legate with other Legion libraries? Yes! If you’re willing to extract the Legion primitives from the LegateStore objects you should be able to pass them into other Legion libraries such as FlexFlow.

  • Does Legate interoperate with X? Yes, probably, but we don’t recommend it. Our motivation for building Legate is to provide the bare minimum subset of functionality that we believe is essential for building truly composable software that can still run at scale. No other systems out there met our needs. Providing interoperability with those other systems will destroy the very essence of what Legate is and significantly dilute its benefits. All that being said, Legion does provide some means of doing stop-the-world exchanges with other runtime system running concurrently in the same processes. If you are interested in pursuing this approach please open an issue on the Legion github issue tracker as it will be almost entirely orthogonal to how you use Legate.


A complete list of available features can is provided in the API reference.

Next Steps

We recommend starting by experimenting with at least one Legate application library to test out performance and see how Legate works. If you are interested in building your own Legate application library, we recommend that you investigate our Legate Hello World application library that provides a small example of how to get started developing your own drop-in replacement library on top of Legion using the Legate Core library.