PGAS languages like and offer a shared memory view for distributed systems. They simplify parallel programming by allowing data access across nodes using a , while maintaining distributed memory performance benefits.
These languages extend C and Fortran with PGAS features, aiming to boost productivity in Exascale computing. They provide a balance between ease of use and performance, addressing challenges in large-scale parallel programming.
Overview of PGAS languages
PGAS (Partitioned Global Address Space) languages provide a shared memory programming model for distributed memory systems, simplifying parallel programming for Exascale computing
PGAS languages allow programmers to access and manipulate data across multiple nodes using a global address space, while maintaining the performance benefits of distributed memory architectures
Two prominent PGAS languages are UPC (Unified Parallel C) and Coarray Fortran, which extend the C and Fortran languages respectively to support PGAS concepts
UPC and Coarray Fortran
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UPC is an extension of the C programming language that incorporates PGAS features, allowing programmers to write parallel code using familiar C syntax and semantics
Coarray Fortran extends Fortran with coarrays, which are distributed arrays that can be accessed and manipulated by multiple processes simultaneously
Both UPC and Coarray Fortran aim to provide a more productive and efficient way to write parallel programs for Exascale systems compared to traditional message passing approaches
Key characteristics of PGAS
PGAS languages provide a global address space that is logically partitioned across multiple processes or threads, enabling each process to access both local and remote data
The global address space is typically divided into private and shared regions, with each process having its own private memory space and a portion of the shared memory space
PGAS languages support one-sided communication, allowing processes to access remote data directly without explicit coordination with the remote process, reducing communication overhead
Synchronization mechanisms are provided to ensure data consistency and prevent race conditions when accessing shared data across multiple processes
Partitioned global address space
The partitioned global address space is a key concept in PGAS languages, enabling a shared memory view of distributed memory systems
In PGAS, the global memory is logically partitioned across multiple processes or nodes, with each process having a portion of the global address space
This partitioning allows for efficient local memory access while still providing a global view of the memory space
Logical partitioning of memory
The global memory space in PGAS is logically divided into partitions, with each partition assigned to a specific process or node
Each process has fast access to its local partition of the global memory, while accessing data in remote partitions may incur communication overhead
The logical partitioning of memory allows programmers to exploit data and minimize remote memory access for improved performance
Local vs global memory access
PGAS languages distinguish between local and global memory access, with local access being faster than global access
Local memory access refers to a process accessing data within its own partition of the global address space, which typically involves no communication overhead
Global memory access involves a process accessing data in a remote partition, which requires communication between processes and may have higher and lower compared to local access
Implications for performance
The performance of PGAS applications depends on the balance between local and global memory access, as well as the efficiency of communication between processes
Minimizing global memory access and optimizing communication patterns can significantly improve the performance of PGAS applications
Proper data distribution and locality-aware programming techniques are crucial for achieving high performance in PGAS languages, especially at Exascale
UPC (Unified Parallel C)
UPC is an extension of the C programming language designed for parallel programming using the PGAS model
UPC adds new keywords, data types, and constructs to the C language to support parallel programming, while maintaining backward compatibility with standard C
Extensions to C language
UPC introduces the
THREADS
keyword to specify the number of threads or processes in a parallel program
The
shared
keyword is used to declare variables that are accessible by all threads in the global address space
UPC also provides synchronization primitives, such as barriers and locks, to coordinate access to shared data and prevent race conditions
PGAS memory model in UPC
In UPC, the global address space is partitioned into shared and private regions, with each thread having its own private memory space and a portion of the shared space
Shared variables are distributed across the threads in a round-robin fashion by default, but programmers can specify custom data layouts using the
layout
keyword
UPC provides pointer-to-shared and pointer-to-local data types to distinguish between pointers that reference shared and private memory, respectively
UPC parallel programming constructs
UPC supports parallel loops using the
upc_forall
construct, which distributes loop iterations across the available threads
The
upc_barrier
function is used to synchronize all threads at a specific point in the program, ensuring that all threads have completed their work before proceeding
UPC also provides functions for collective communication, such as
upc_all_broadcast
and
upc_all_reduce
, which perform operations across all threads
UPC shared vs private variables
UPC distinguishes between shared and private variables, with shared variables accessible by all threads and private variables only accessible by the owning thread
Shared variables are declared using the
shared
keyword and are distributed across the threads in the global address space
Private variables are declared without the
shared
keyword and are only accessible within the local memory space of each thread
Synchronization mechanisms in UPC
UPC provides various synchronization mechanisms to ensure data consistency and prevent race conditions when accessing shared variables
UPC supports barriers, which synchronize all threads at a specific point in the program, ensuring that all threads have completed their work before proceeding
Locks, such as
upc_lock_t
, are used to protect critical sections of code and prevent multiple threads from simultaneously accessing shared data
UPC also provides non-blocking communication primitives, such as
upc_memput_async
and
upc_memget_async
, which allow for overlapping computation and communication
Coarray Fortran
Coarray Fortran is an extension of the Fortran programming language that supports PGAS programming using coarrays
Coarrays are distributed arrays that can be accessed and manipulated by multiple processes simultaneously, providing a shared memory view of distributed data
Fortran extensions for PGAS
Coarray Fortran introduces the
codimension
keyword to declare coarrays, which are arrays that are distributed across multiple processes
The
sync
keyword is used to synchronize access to coarrays, ensuring data consistency and preventing race conditions
Coarray Fortran also provides intrinsic functions for communication and synchronization, such as
co_sum
and
co_broadcast
Coarray syntax and semantics
Coarrays are declared using the
codimension
keyword followed by the dimensions of the coarray in square brackets
Each process has its own local instance of a coarray, and the
codimension
specifies the distribution of the coarray across the processes
Coarray elements can be accessed using the usual array indexing syntax, with the addition of a
codimension
index to specify the remote process
Coarray data distribution
Coarray Fortran allows programmers to specify the distribution of coarray elements across the processes using the
codimension
declaration
By default, coarrays are distributed in a block fashion, with each process receiving a contiguous block of elements
Programmers can also specify custom data distributions using the
cobounds
directive, which allows for more control over the distribution of coarray elements
Synchronization with coarrays
Coarray Fortran provides synchronization mechanisms to ensure data consistency and prevent race conditions when accessing coarray elements
The
sync
keyword is used to synchronize access to coarrays, ensuring that all processes have completed their updates before any process can access the data
The
sync all
statement synchronizes all processes, while
sync images
synchronizes a subset of processes specified by an integer array
Coarray Fortran also provides critical sections and locks for more fine-grained synchronization of shared data access
Performance considerations
Achieving high performance in PGAS languages requires careful consideration of data distribution, communication patterns, and synchronization
Minimizing remote memory access, optimizing communication, and balancing computation and communication are key factors in maximizing the performance of PGAS applications
Minimizing remote memory access
Remote memory access in PGAS languages typically incurs higher latency and lower bandwidth compared to local memory access
To minimize remote memory access, programmers should strive to distribute data across processes in a way that maximizes local access and reduces the need for remote communication
Techniques such as data replication, caching, and prefetching can help reduce the impact of remote memory access on application performance
Optimizing communication patterns
Efficient communication is crucial for the performance of PGAS applications, especially at large scales
Programmers should aim to minimize the number and size of messages exchanged between processes, using techniques such as message aggregation and collective communication operations
Overlapping computation and communication can help hide communication latency and improve overall application performance
Balancing computation and communication
Achieving a balance between computation and communication is essential for the scalability and performance of PGAS applications
Programmers should aim to distribute the computational workload evenly across processes while minimizing the communication overhead
Techniques such as load balancing, asynchronous communication, and communication-computation overlap can help achieve a better balance and improve application performance
Scalability of PGAS applications
The scalability of PGAS applications depends on various factors, including the problem size, data distribution, communication patterns, and synchronization requirements
To ensure good scalability, programmers should aim to minimize global synchronization, exploit data locality, and use efficient communication primitives
Proper performance analysis and tuning are essential for identifying and addressing scalability bottlenecks in PGAS applications
Comparison of UPC and Coarray Fortran
UPC and Coarray Fortran are both PGAS languages but differ in their language features, syntax, and performance characteristics
Understanding the differences between these languages can help programmers choose the most suitable language for their specific application and performance requirements
Language features and syntax
UPC is based on the C programming language and extends it with PGAS features using keywords such as
shared
and
upc_forall
Coarray Fortran, on the other hand, extends Fortran with coarrays and uses keywords such as
codimension
and
sync
The syntax and programming style of UPC and Coarray Fortran reflect their respective base languages, which may influence the choice of language for programmers with different backgrounds
Performance tradeoffs
The performance of UPC and Coarray Fortran applications can vary depending on factors such as the problem size, data distribution, communication patterns, and compiler optimizations
UPC's performance is often influenced by the efficiency of its shared memory access and the overhead of its synchronization primitives
Coarray Fortran's performance depends on the efficiency of its coarray communication and synchronization, as well as the optimization capabilities of the Fortran compiler
Comparative studies have shown that the performance of UPC and Coarray Fortran can be similar for certain applications, but the specific performance characteristics may vary depending on the problem and the implementation details
Interoperability with other languages
Both UPC and Coarray Fortran can interoperate with other programming languages and parallel programming models, such as MPI and OpenMP
UPC can interface with C and C++ code, allowing programmers to leverage existing libraries and code bases
Coarray Fortran can interoperate with other Fortran code and can also interface with C and other languages using Fortran's interoperability features
The interoperability of UPC and Coarray Fortran with other languages and programming models is important for integrating PGAS into existing applications and workflows
PGAS vs message passing
PGAS languages, such as UPC and Coarray Fortran, offer an alternative to traditional message passing models like MPI for parallel programming
Understanding the differences between PGAS and message passing can help programmers choose the most appropriate programming model for their specific application and performance requirements
Productivity and ease of use
PGAS languages aim to provide a more productive and user-friendly programming model compared to message passing
The shared memory abstraction in PGAS allows programmers to access and manipulate distributed data using familiar programming constructs, such as arrays and pointers
PGAS languages often require less explicit communication and synchronization compared to message passing, which can simplify the development of parallel applications
However, the learning curve for PGAS languages may be steeper for programmers who are already familiar with message passing models like MPI
Performance at scale
The performance of PGAS and message passing applications at scale depends on various factors, such as the problem size, communication patterns, and hardware characteristics
Message passing models like MPI have been widely used and optimized for large-scale parallel applications, with extensive support for efficient communication and synchronization primitives
PGAS languages, while offering productivity advantages, may face challenges in terms of performance at extreme scales due to the overhead of remote memory access and the need for efficient synchronization
The scalability of PGAS applications depends on the ability to minimize remote memory access, optimize communication patterns, and leverage hardware support for efficient PGAS operations
Suitability for different problem domains
The choice between PGAS and message passing depends on the specific characteristics of the problem domain and the application requirements
PGAS languages are well-suited for applications with irregular data structures, dynamic communication patterns, and fine-grained data sharing, such as graph algorithms and adaptive mesh refinement
Message passing models like MPI are often preferred for applications with regular communication patterns, bulk synchronous parallelism, and coarse-grained data exchange, such as stencil computations and matrix operations
Hybrid programming models that combine PGAS and message passing can offer the best of both worlds, allowing programmers to leverage the strengths of each model for different parts of the application
Advanced topics in PGAS
As PGAS languages continue to evolve and mature, several advanced topics have emerged that are relevant for Exascale computing and beyond
These topics include hybrid programming, support for irregular data structures, fault tolerance, and the integration of PGAS with other parallel programming models
Hybrid programming with PGAS and MPI
Hybrid programming models that combine PGAS languages with message passing models like MPI can offer the benefits of both approaches
In a hybrid PGAS-MPI model, PGAS can be used for fine-grained, irregular communication within a node, while MPI can be used for coarse-grained, regular communication between nodes
Hybrid programming can help optimize the performance and scalability of PGAS applications by leveraging the strengths of each programming model for different aspects of the application
However, hybrid programming also introduces additional complexity and requires careful design and tuning to achieve optimal performance
Irregular data structures in PGAS
PGAS languages have been traditionally used for applications with regular data structures and communication patterns, but there is growing interest in supporting irregular data structures
Irregular data structures, such as graphs and unstructured meshes, pose challenges for PGAS languages due to their dynamic nature and non-uniform data access patterns
Research efforts have focused on extending PGAS languages with support for irregular data structures, such as global pointers, distributed containers, and partitioned global address space maps
Efficient support for irregular data structures in PGAS can enable a wider range of applications to benefit from the productivity and performance advantages of PGAS programming
Fault tolerance in PGAS applications
Fault tolerance is a critical concern for Exascale computing, as the increasing scale and complexity of systems make failures more likely
PGAS languages face challenges in providing efficient fault tolerance mechanisms due to their global address space abstraction and the need for consistent data access across processes
Research efforts have explored various fault tolerance techniques for PGAS, such as checkpoint-restart, message logging, and redundant computation
Integrating fault tolerance into PGAS languages and applications requires careful consideration of the trade-offs between performance, scalability, and resilience, as well as the development of efficient and transparent fault tolerance mechanisms