Stanford University logo SLAC National Accelerator Laboratory logo Los Alamos National Laboratory logo NVIDIA logo Winner of the R&D 100 Award


A Data-Centric Parallel Programming System


Legion Overview


Modern computer architectures are increasingly composed of heterogeneous processors and deep complex memory hierarchies. Furthermore, the cost of data movement within these architectures is now coming to dominate the overall cost of computation, both in terms of power and performance. Despite these conditions, most machines are still programmed using an eclectic mix of programming systems that focus only on describing parallelism (MPI,Pthreads, OpenMP,OpenCL,OpenACC,CUDA). Achieving high performance and power efficiency on future architectures will require programming systems capable of reasoning about the structure of program data to facilitate efficient placement and movement of data.

Programming Model

Legion is a data-centric programming model for writing high-performance applications for distributed heterogeneous architectures. Making the programming system aware of the structure of program data gives Legion programs three advantages:

  • User-Specification of Data Properties: Legion provides abstractions for programmers to explicitly declare properties of program data including organization, partitioning, privileges, and coherence. Unlike current programming systems in which these properties are implicitly managed by programmers, Legion makes them explicit and provides the implementation for programmers.
  • Automated Mechanisms: current programming models require developers to explicitly specify parallelism and issue data movement operations. Both responsibilities can easily lead to the introduction of bugs in complex applications. By understanding the structure of program data and how it is used, Legion can implicitly extract parallelism and issue the necessary data movement operations in accordance with the application-specified data properties, thereby removing a significant burden from the programmer.
  • User-Controlled Mapping: by providing abstractions for representing both tasks and data, Legion makes it easy to describe how to map applications onto different architectures. Legion provides a mapping interface which gives programmers direct control over all the details of how an application is mapped and executed. Furthermore, Legion’s understanding of program data makes the mapping process orthogonal to correctness. This simplifies program performance tuning and enables easy porting of applications to new architectures.

There are three important abstractions in the Legion programming model:

  • Logical Regions: Logical regions are the fundamental abstraction used for describing program data in Legion applications. Logical regions support a relational model for data. Each logical region is described by an index space of rows (either unstructured pointers or structured 1D, 2D, or 3D arrays) and a field space of columns. Unlike other relational models, Legion supports a different class of operations on logical regions: logical regions can be arbitrarily partitioned into sub-regions based on index space or sliced on their field space. Data structures can be encoded in logical regions to express locality with partitioning and slicing describing data independence.
  • Tree of Tasks using Regions: Every Legion program executes as a tree of tasks with a top-level task spawning sub-tasks which can recursively spawn further sub-tasks. All tasks in Legion must specify the logical regions they will access as well as the privileges and coherence for each logical region. Legion enforces a functional requirement on privileges which enables a hierarchical and distributed scheduling algorithm that is essential for scalability.
  • Mapping Interface: Legion makes no implicit decisions concerning how applications are mapped onto target hardware. Instead mapping decisions regarding how tasks are assigned to processors and how physical instances of logical regions are assigned to memories are made entirely by mappers. Mappers are part of application code and implement a mapping interface. Mappers are queried by the Legion runtime whenever any mapping decision needs to be made. Legion guarantees that mapping decisions only impact performance and are orthogonal to correctness which simplifies tuning of Legion applications and enables easy porting to different architectures.

There are many more details to the Legion programming model and we encourage you to learn more about them by reading our publications.

Target Users

Legion is designed for two classes of users:

  • Advanced application developers: programmers who traditionally have used combinations of MPI, GASNet, Pthreads, OpenCL, and/or CUDA to develop their applications and always re-write applications from scratch for maximum performance on each new architecture.
  • Domain specific language and library authors: tool writers who develop high-level productivity languages and libraries that support separate implementations for every target architecture for maximum performance.

In both cases, Legion provides a common framework for implementing applications which can achieve portable performance across a range of architectures. The target class of users also dictates that productivity in Legion will always be a second-class design constraint behind performance. Instead Legion is designed to be extensible and to support higher-level productivity languages and libraries.

Design Principles

There are several principles which have driven the design and implementation of Legion based on our own experience writing applications for the target class of architectures.

  • User control of decomposition: it is impossible to develop a general algorithm capable of automatically inferring the optimal decomposition of data and computation for every program. Consequently the Legion programming model explicitly puts control of how data is decomposed into logical regions and how algorithms are decomposed into tasks in the hands of the application developer.
  • Handle irregularity: Legion is designed so that all decisions can be made dynamically to handle irregularity. This includes the ability to make dynamic decisions regarding how data is partitioned, algorithms are decomposed into tasks, where data and tasks are placed, how hard and soft-errors are managed, etc.
  • Hybrid programming model: tasks in Legion are functional with controlled side effects on logical regions which the Legion runtime can understand. Individual tasks consist of traditional imperative code. The coarse-grained functional model enables Legion’s distributed scheduling algorithm, while still supporting fine-grained imperative code within tasks that is more familiar to most application developers.
  • Deferred execution: all runtime calls in Legion are deferred which means that they can be launched asynchronously and Legion is responsible for computing the necessary dependences and not performing operations until it is safe to do so. This is only possible because Legion understands the structure of program data for deferring data movement operations and because Legion can reason about task’s side-effects on logical regions.
  • Provide mechanism but not policy: Legion is designed to give programmers total control over the policy of how an application is executed, while still automating any operations which can uniquely be inferred from the given policy. For example, Legion provides total control over where tasks and data are run, but the runtime automatically infers the necessary copies and data movement operations to conform to the specified privilege and coherence annotations on the logical regions arguments to each task.
  • Decouple correctness from performance: In conjunction with the previous design principle, Legion ensures that policy decisions never impact the correctness of an application. Policy decisions about how to map applications only impact performance allowing applications to customize mappings to particular architectures without needing to be concerned with affecting correctness.

In addition to our design principles for Legion, there were are several challenges which we explicitly avoided in designing Legion.

  • Leaf task generator: Legion is not designed to solve the problem of emitting high performance leaf tasks for heterogeneous processors. Legion aids in managing multiple functionally-equivalent variants of a task on different processor kinds, but does not help with implementing them. Current Legion applications use kernels that have either been hand-written for each processor kind or JIT-compiled with Terra.
  • Magic mapper/performance optimizer: while Legion provides a default mapper, it will never be possible for it to perform an optimal mapping for all applications. High performance will require custom mappings. The Legion mapper interface is intentionally extensible to support both custom mappers, and the creation of mapping tools for building custom mappers.
  • Automatic decomposition: decisions regarding how and at what granularity to partition logical regions is the responsibility of the application. Similarly the choice of how to decompose algorithms into tasks is also the responsibility of the application. In both cases, Legion could never make the correct decisions for all applications and architectures.
  • High productivity: based on the target users for Legion productivity will always be a second-class design consideration for the Legion. Instead Legion is designed to be extensible and targeted by higher-level productivity compilers and libraries.

System Architecture

The above figure shows the architecture of the Legion programming system. Applications targeting Legion have the option of either being written in the Regent programming language or written directly to the Legion C++ runtime interface. Applications written in Regent are compiled to LLVM (and call a C wrapper for the C++ runtime API).

The Legion runtime system implements the Legion programming model and supports all the necessary API calls for writing Legion applications. Mappers are special C++ objects that are built on top of the Legion mapping interface which is queried by the Legion runtime system to make all mapping decisions when executing a Legion program. Applications can either chose to use the default Legion mapper or write custom mappers for higher performance.

The Legion runtime system sits on top of a low-level runtime interface called Realm. The Realm interface is designed to provide portability to the entire Legion system by providing primitives which can be implemented on a wide range of architectures. Realm is a modular runtime and supports a variety of underlying technologies for portability across a variety of machines, including GASNet for high-performance networking on a variety of interconnects and CUDA for GPUs.