Glossary

5.4 Advanced MPI Concepts and Performance Optimization

5 min read•july 30, 2024

MPI's advanced concepts take distributed memory programming to the next level. and offer new ways to boost performance, while optimization techniques help squeeze out every bit of efficiency from your code.

Mastering these advanced MPI features can make your programs run faster and scale better. From fine-tuning communication patterns to leveraging network topologies, these tools give you the power to tackle even the most demanding parallel computing challenges.

Advanced MPI Concepts

One-Sided Communication

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  • One-sided communication allows remote memory access (RMA) operations without explicit involvement of the target process
  • MPI window objects expose local memory for RMA operations
  • Key functions for one-sided communication include: 
    • [MPI_Put](https://www.fiveableKeyTerm:mpi_put)
      transfers data from origin to target process
    • [MPI_Get](https://www.fiveableKeyTerm:mpi_get)
      retrieves data from target to origin process
    • [MPI_Accumulate](https://www.fiveableKeyTerm:mpi_accumulate)
      updates target memory with combination of local and remote data
  • Synchronization modes control access epochs:
    • (fence, post-start-complete-wait)
    • (lock, unlock)
  • Benefits include reduced synchronization overhead and potential for overlap of communication and computation

Parallel I/O

  • Enables concurrent file access by multiple processes, improving I/O performance in large-scale applications
  • MPI-IO provides collective I/O operations (, ) optimizing data access patterns
  • File views allow processes to access non-contiguous file regions efficiently
  • Non-blocking I/O operations overlap computation and I/O, potentially improving overall application performance
  • aggregates multiple small I/O requests into larger operations, reducing overhead
  • separates I/O into communication and I/O phases, optimizing collective operations
  • Hints mechanism allows fine-tuning of I/O performance (buffer sizes, striping parameters)

MPI Program Optimization

Performance Analysis Tools

  • identify performance bottlenecks in MPI programs:
    • provides lightweight statistical profiling
    • offers scalable performance analysis for large-scale systems
    • visualizes communication patterns and timelines
  • Trace-based tools capture detailed event information for post-mortem analysis
  • Hardware performance counters measure low-level system events (cache misses, floating-point operations)
  • Automated bottleneck detection algorithms identify performance issues in large-scale applications

Communication Optimization

  • Analyze and optimize communication patterns:
    • Replace point-to-point with collective operations where applicable
    • Use non-blocking operations to overlap computation and communication
  • reduce small message overheads:
    • Combine multiple small messages into larger buffers
    • Use derived datatypes to describe non-contiguous data layouts
  • and tuning improves performance:
    • Hierarchical algorithms for large process counts
    • Topology-aware implementations leverage network structure
  • reduce memory footprint and copying overhead:
    • In-place operations for
    • Zero-copy protocols for large messages

System-Level Optimization

  • Mitigate system noise and OS jitter effects:
    • dedicates cores to MPI processes
    • reduce timer resolution issues
  • Optimize process placement and binding:
    • Use topology information to minimize inter-node communication
    • Exploit shared caches and NUMA domains for improved data locality
  • Tune MPI runtime parameters:
    • Adjust eager/rendezvous protocol thresholds
    • Configure progression threads for asynchronous progress

Network Topology Impact

Network Architectures

  • Common HPC network topologies affect communication patterns and performance:
    • Fat-tree provides high bisection bandwidth (InfiniBand clusters)
    • Torus offers low diameter and good scalability (Blue Gene systems)
    • Dragonfly combines low and high bandwidth (Cray XC series)
  • Network characteristics influence optimal communication strategies:
    • Latency determines effectiveness of message aggregation
    • Bandwidth impacts choice between eager and
  • Routing algorithms affect congestion and :
    • dynamically adjusts to network conditions
    • provides predictable performance but may suffer from hotspots

Process Mapping Strategies

  • Process mapping significantly impacts communication locality and overall application performance:
    • Compact mapping groups nearby ranks on same node (reduces inter-node communication)
    • Scatter mapping distributes ranks across nodes (improves load balance)
    • Round-robin mapping balances intra-node and inter-node communication
  • in process placement improves memory access patterns:
    • Align processes with NUMA domains to reduce remote memory accesses
    • Use 
      hwloc
      library for portable topology discovery and process binding
  • MPI topology functions help applications adapt to underlying hardware: 
    • [MPI_Dist_graph_create_adjacent](https://www.fiveableKeyTerm:mpi_dist_graph_create_adjacent)
      creates custom communication graphs
    • [MPI_Cart_create](https://www.fiveableKeyTerm:mpi_cart_create)
      maps processes to Cartesian topologies

Topology-Aware Optimizations

  • Collective operations leverage network structure for optimal performance:
    • for power-of-two process counts
    • for non-power-of-two counts
  • Virtual topology mapping aligns application communication patterns with physical network:
    • minimize communication volume
    • Topology-aware rank reordering reduces network congestion
  • Network congestion mitigation techniques:
    • prevents network saturation
    • avoids contention on shared links

Load Balancing with MPI

Dynamic Load Balancing Techniques

  • balances workload by allowing idle processes to take work from busy ones:
    • Implement using one-sided operations for efficient task queues
    • Use randomized stealing to reduce contention
  • Task pools distribute work dynamically:
    • for small-scale systems
    • for improved scalability
  • Hierarchical load balancing strategies balance workloads across system levels:
    • using shared memory
    • using MPI communication
  • adjusts workload distribution based on runtime metrics:
    • Recursive bisection for regular domains
    • Space-filling curves for irregular domains

Load Monitoring and Redistribution

  • Implement load monitoring using MPI collective operations: 
    • MPI_Allgather
      to collect workload information
    • MPI_Reduce
      to compute global load statistics
  • Workload redistribution strategies:
    • for gradual load balancing
    • Dimension exchange for hypercube topologies
  • Consider data locality and communication costs when redistributing work:
    • Use cost models to estimate redistribution overhead
    • Employ data migration techniques to maintain locality

Hybrid Programming Models

  • Combine MPI with shared-memory parallelism for flexible load balancing:
    • MPI+OpenMP allows fine-grained load balancing within nodes
    • MPI+CUDA enables GPU workload distribution
  • Implement multi-level load balancing:
    • Coarse-grained balancing with MPI across nodes
    • Fine-grained balancing with threads within nodes
  • Asynchronous progress engines improve responsiveness:
    • Dedicated communication threads handle MPI operations
    • Overlap computation and communication for better efficiency

Key Terms to Review (61)

Active target synchronization: Active target synchronization is a communication pattern in parallel computing where processes actively synchronize with specific target processes to coordinate data exchanges efficiently. This approach is particularly important for optimizing performance, as it minimizes idle time and maximizes the use of network bandwidth by allowing targeted communication instead of relying on a broadcast method. By focusing on direct interactions, active target synchronization enhances data transfer speeds and reduces overall latency in message-passing environments.
Adaptive domain decomposition: Adaptive domain decomposition is a technique used in parallel computing to partition a computational domain into subdomains that can be assigned to different processors, optimizing load balancing and resource utilization. This approach allows for dynamic adjustments of the subdomain sizes based on the computational workload, ensuring that all processors are efficiently utilized and minimizing idle time. By adapting the distribution of work, this method enhances performance, particularly in applications with irregular or varying workloads.
Adaptive Routing: Adaptive routing is a dynamic method of determining the optimal path for data packets in a network, adjusting routes based on current network conditions like congestion or link failures. This technique enhances the efficiency and reliability of data transmission by continuously analyzing and adapting to changes in the network environment, making it particularly relevant in high-performance computing contexts.
Binomial Tree Algorithms: Binomial tree algorithms are a type of data structure that enables efficient parallel computations, leveraging the properties of binomial trees to facilitate communication patterns and collective operations in distributed systems. They play a crucial role in optimizing performance in message passing interfaces by minimizing communication overhead and maximizing data throughput. These algorithms utilize the hierarchical structure of binomial trees to efficiently combine results from multiple processes.
Buffer management strategies: Buffer management strategies are techniques used to efficiently handle data storage and retrieval in parallel and distributed computing systems, ensuring that data flows smoothly between different components. These strategies are crucial for optimizing performance, as they help manage the timing and availability of data, reducing latency and preventing bottlenecks. Effective buffer management can lead to better resource utilization and improved overall system throughput.
Centralized pools: Centralized pools refer to a system where resources, such as memory or processing power, are aggregated in one location to be shared among multiple processes or tasks. This approach allows for efficient resource management and improved performance by minimizing duplication and enabling better coordination among tasks that require similar resources.
Checkpointing: Checkpointing is a fault tolerance technique used in computing systems, particularly in parallel and distributed environments, to save the state of a system at specific intervals. This process allows the system to recover from failures by reverting back to the last saved state, minimizing data loss and reducing the time needed to recover from errors.
Collective Algorithm Selection: Collective algorithm selection refers to the process of choosing the most efficient communication algorithm to be used by multiple processes in parallel computing, particularly in MPI (Message Passing Interface). This selection is crucial for optimizing performance, as different algorithms can significantly impact the speed and efficiency of data transfer among processes, especially in large-scale parallel applications. Effective collective algorithm selection considers factors such as network topology, message size, and the number of processes involved.
Collective Communication: Collective communication refers to the communication patterns in parallel computing where a group of processes exchange data simultaneously, rather than engaging in one-to-one messaging. This approach is essential for efficiently managing data sharing and synchronization among multiple processes, making it fundamental to the performance of distributed applications. By allowing a set of processes to communicate collectively, it enhances scalability and reduces the overhead that comes with point-to-point communications.
Communication scheduling: Communication scheduling refers to the process of organizing and optimizing data transfer among processes in a parallel computing environment to improve performance and resource utilization. Effective communication scheduling is critical as it minimizes the waiting time for data exchanges, enhances throughput, and reduces overall execution time, particularly in message-passing interfaces like MPI.
Core specialization: Core specialization refers to the process of optimizing and tailoring computational tasks to specific cores in a multi-core processing environment. This approach aims to enhance performance by ensuring that different processing units handle tasks that best fit their capabilities, leading to better resource utilization and reduced contention among cores.
Data sieving: Data sieving is a technique used in parallel computing to optimize the process of reading and writing large amounts of data by minimizing the number of I/O operations. This method involves collecting requests for data from multiple processes and then performing fewer, larger read or write operations, which enhances performance by reducing overhead and improving data locality. It’s especially relevant in high-performance computing environments where efficient data handling can significantly affect overall system performance.
Diffusion-based methods: Diffusion-based methods are computational strategies used to distribute workloads efficiently across multiple processing units, ensuring balanced resource utilization and minimizing processing time. These methods leverage the concept of diffusion, where tasks are spread out over a network to optimize performance and reduce bottlenecks. This approach connects closely with advanced communication protocols and load balancing techniques, as it focuses on adapting workload distribution dynamically based on current system conditions.
Distributed pools: Distributed pools refer to a collection of resources that are spread across multiple nodes or locations in a distributed system, enabling efficient resource management and utilization. This concept is important for optimizing performance and scalability in parallel computing, as it allows tasks to access resources that are not confined to a single location, thus improving load balancing and reducing bottlenecks.
Dragonfly network topology: Dragonfly network topology is a type of interconnection architecture designed for high-performance computing systems, particularly in large-scale parallel processing environments. It efficiently connects multiple groups of nodes using a layered and hierarchical approach, reducing latency and improving bandwidth. This structure is crucial for optimizing performance in distributed systems, where communication speed and efficiency are vital.
Eager Protocols: Eager protocols are communication methods used in parallel computing to send messages from one process to another before the sender has completed its operation. This proactive approach helps to optimize performance by allowing overlapping of computation and communication, which is crucial for efficient data handling in distributed systems. The main idea is to reduce latency by initiating message transfers earlier, thus enhancing overall system throughput and resource utilization.
Fat-tree network topology: Fat-tree network topology is a type of data center networking architecture designed to provide high bandwidth and low latency by structuring switches in a hierarchical manner. This design allows for efficient communication between servers and minimizes congestion, making it ideal for parallel and distributed computing environments.
Gprof: gprof is a performance analysis tool used to profile the performance of programs, particularly in C and C++ languages. It helps developers identify where time is being spent in their code, providing valuable insights into function call statistics and execution times. By enabling better performance optimization, gprof plays a crucial role in understanding program efficiency in both single and parallel computing environments.
Graph partitioning algorithms: Graph partitioning algorithms are techniques used to divide a graph into smaller, more manageable subgraphs while minimizing the number of edges that cross between them. These algorithms are crucial for optimizing parallel processing, as they enhance load balancing and reduce communication costs among distributed systems. Efficient graph partitioning can lead to significant improvements in performance and scalability in applications such as scientific computing and network analysis.
Hybrid Programming Model: A hybrid programming model combines different parallel programming paradigms, typically threading and message passing, to leverage the strengths of each for performance optimization in parallel computing. This model allows for more efficient use of system resources by integrating shared memory techniques with distributed memory approaches, enabling better scalability and flexibility in designing parallel applications.
Intel Trace Analyzer: Intel Trace Analyzer is a powerful performance analysis tool designed for applications running on parallel and distributed systems, specifically those using the Message Passing Interface (MPI). It helps developers identify performance bottlenecks, analyze communication patterns, and visualize the execution of their parallel applications. By providing detailed insights into MPI communication, the tool supports optimization efforts to enhance application performance and scalability.
Inter-node balancing: Inter-node balancing refers to the method of distributing workloads evenly across multiple computing nodes in a parallel or distributed system. This technique aims to optimize resource utilization and reduce processing time by ensuring that no single node is overwhelmed while others remain underutilized, which is especially crucial in high-performance computing environments.
Intra-node balancing: Intra-node balancing refers to the optimization process that ensures even distribution of computational workload across multiple cores or processors within a single node in a parallel computing environment. This concept is essential for maximizing resource utilization and minimizing idle time, which ultimately improves overall application performance. Proper intra-node balancing helps to avoid bottlenecks caused by uneven task distribution, leading to more efficient execution of parallel applications.
Latency: Latency is the time delay experienced in a system when transferring data from one point to another, often measured in milliseconds. It is a crucial factor in determining the performance and efficiency of computing systems, especially in parallel and distributed computing environments where communication between processes can significantly impact overall execution time.
Load Balancing: Load balancing is the process of distributing workloads across multiple computing resources to optimize resource use, minimize response time, and avoid overload of any single resource. This technique is essential in maximizing performance in both parallel and distributed computing environments, ensuring that tasks are allocated efficiently among available processors or nodes.
Map-Reduce: Map-Reduce is a programming model designed for processing large data sets across distributed systems. It divides tasks into two main functions: 'map', which processes input data and produces key-value pairs, and 'reduce', which aggregates those pairs to produce a final output. This model is vital for efficient data processing in parallel computing, ensuring scalability and performance optimization.
Message aggregation techniques: Message aggregation techniques are methods used in parallel and distributed computing to combine multiple messages into a single message, reducing communication overhead and improving overall system performance. These techniques are crucial for optimizing communication patterns in high-performance computing applications, as they can help minimize latency and bandwidth consumption. Effective message aggregation leads to better resource utilization and enhanced scalability in distributed systems.
Message throttling: Message throttling is a control mechanism used in distributed computing to manage the rate of message transmission between processes or nodes. This technique helps prevent network congestion and ensures that systems can handle incoming messages efficiently without overwhelming resources. By regulating message flow, it enhances overall system performance and stability.
MPI Communicators: MPI communicators are essential structures in the Message Passing Interface (MPI) that define the scope of communication between processes in parallel and distributed computing. They allow processes to communicate with each other while controlling which processes are involved in a specific communication operation. This facilitates organized data exchange and enhances performance by limiting the scope of communications, thus reducing overhead.
MPI Derived Data Types: MPI derived data types are custom data types defined in the Message Passing Interface (MPI) that allow users to group multiple different types of data into a single entity for communication between processes. These types are especially useful in parallel computing, as they enable efficient data exchange and reduce the overhead associated with sending complex data structures. By using derived data types, programmers can optimize communication patterns and enhance performance when dealing with structured data.
Mpi_accumulate: The `mpi_accumulate` function in MPI (Message Passing Interface) is used for performing a reduction operation on data from multiple processes and storing the result in a specified location. This function allows for efficient collective communication by combining values from different sources, applying an operation such as sum, max, or min, and ensuring that the result is stored in a designated buffer on one of the participating processes. It plays a crucial role in optimizing performance and reducing communication overhead in parallel applications.
Mpi_bcast: The `mpi_bcast` function is a collective communication operation in the Message Passing Interface (MPI) that allows one process to send a message to all other processes within a specified communicator. This operation is crucial for parallel programming, as it ensures that all processes have access to the same data, enabling synchronized actions across multiple nodes.
Mpi_cart_create: The function `mpi_cart_create` is used in MPI (Message Passing Interface) to create a Cartesian topology for a communicator, allowing processes to be arranged in a multi-dimensional grid. This function is essential for optimizing data communication patterns among processes by defining logical relationships based on their geometrical arrangement, enhancing performance in parallel computations.
Mpi_dist_graph_create_adjacent: The `mpi_dist_graph_create_adjacent` function in MPI is used to create a distributed graph topology for processes in a parallel application, allowing for more efficient communication patterns. This function specifically enables the specification of adjacent nodes in the graph, which directly influences how data is exchanged between processes. The design of the graph impacts load balancing, communication overhead, and overall performance in parallel computing tasks.
Mpi_file_read_all: The `mpi_file_read_all` function is a collective operation in the Message Passing Interface (MPI) used to read data from a file simultaneously across all processes in a communicator. This function allows for synchronized reading, ensuring that every participating process retrieves the same data from the file, which can significantly improve efficiency and consistency when handling large data sets in parallel computing environments.
Mpi_file_write_all: The function `mpi_file_write_all` is a collective I/O operation in MPI (Message Passing Interface) that allows multiple processes to write data to a file simultaneously. This function ensures that all processes involved will participate in the write operation, and it guarantees that the data is written in a consistent manner, making it essential for applications that require synchronization and coordination when handling file I/O across different nodes.
Mpi_get: The `mpi_get` function is a communication routine in the Message Passing Interface (MPI) that enables a process to retrieve data from another process's memory. This operation is critical for distributed computing, allowing for data sharing and synchronization between different processes while optimizing performance through efficient communication patterns.
Mpi_put: The `mpi_put` function is a non-blocking point-to-point communication operation in the Message Passing Interface (MPI) that allows a process to directly write data into the memory of another process. This operation is crucial for optimizing data transfers, especially in high-performance computing, as it enables more efficient memory management and reduces synchronization delays between processes.
Mpi_sendrecv: The `mpi_sendrecv` function is an MPI (Message Passing Interface) call that simultaneously sends and receives messages between processes in a parallel computing environment. It is particularly useful for implementing point-to-point communication patterns, allowing for efficient data exchange without the need for separate send and receive operations. By combining these two actions, `mpi_sendrecv` minimizes overhead and optimizes performance in distributed systems.
Mpip: mpip stands for MPI Profiling Interface, which is an extension of the Message Passing Interface (MPI) designed for performance analysis and debugging of parallel applications. It allows developers to gather performance data and insights about their MPI programs, making it easier to identify bottlenecks and optimize communication patterns, which are critical for enhancing overall system performance and minimizing communication overhead.
NUMA Awareness: NUMA Awareness refers to the understanding and optimization of memory access patterns in a Non-Uniform Memory Access (NUMA) architecture, where the time it takes to access memory varies based on the processor's location relative to that memory. In this context, ensuring that processes are scheduled in a way that takes advantage of local memory access can significantly improve performance and reduce latency in parallel computing applications, especially when using message-passing interfaces.
One-sided communication: One-sided communication refers to a method of data exchange in parallel computing where one process can send or receive data without requiring the explicit involvement of the other process. This model allows a sender to initiate communication and proceed with its computation while the receiver may handle the incoming data independently, leading to improved performance and efficiency. This concept plays a vital role in optimizing data transfers and managing resources effectively in distributed systems.
Overlapping computation and communication: Overlapping computation and communication refers to the ability to perform calculations while simultaneously sending or receiving data in a parallel computing environment. This technique is crucial for optimizing performance, as it allows systems to make better use of their resources and minimize idle time, leading to faster overall execution. Efficiently overlapping these two processes can significantly enhance the throughput of parallel programs by keeping all parts of the system busy.
Parallel I/O: Parallel I/O refers to the simultaneous input and output operations that allow multiple processes to read from and write to storage devices at the same time. This technique is crucial for optimizing performance in high-performance computing environments, enabling systems to handle large data sets efficiently by reducing bottlenecks associated with serial I/O operations.
Parallel reduction: Parallel reduction is a computational operation that aggregates data across multiple processes in a parallel computing environment, often utilizing a tree-like structure for efficient computation. It is crucial for performing operations like summing values or finding minimums and maximums across distributed datasets, significantly optimizing performance in high-performance computing applications. By minimizing communication overhead and balancing workloads, parallel reduction enables effective scalability of algorithms and enhances overall efficiency.
Passive Target Synchronization: Passive target synchronization is a method in parallel computing where the receiving process does not actively wait for a message but rather checks for its arrival at certain intervals. This technique allows processes to continue executing other tasks while monitoring for incoming data, improving overall efficiency and resource utilization. It is particularly relevant in optimizing communication patterns and minimizing idle time in a distributed system.
Point-to-Point Communication: Point-to-point communication refers to the direct exchange of messages between two specific processes or nodes in a distributed system. This type of communication is crucial for enabling collaboration and data transfer in parallel computing environments, allowing for efficient interactions and coordination between processes that may be located on different machines or cores. Understanding point-to-point communication is essential for mastering message passing programming models, implementing the Message Passing Interface (MPI), optimizing performance, and developing complex communication patterns.
Process mapping strategies: Process mapping strategies refer to the techniques used to visualize and analyze the flow of processes in a parallel and distributed computing environment. These strategies help identify inefficiencies, bottlenecks, and areas for optimization, which are crucial for improving performance in systems utilizing MPI (Message Passing Interface). By understanding how processes interact and communicate, developers can optimize resource allocation, reduce latency, and enhance overall system throughput.
Profiling tools: Profiling tools are software utilities designed to analyze a program's execution behavior, helping developers identify performance bottlenecks and optimize resource usage. These tools provide insights into various aspects such as CPU usage, memory allocation, and thread performance, enabling programmers to fine-tune their applications for better efficiency and scalability in different computing environments.
Recursive doubling algorithms: Recursive doubling algorithms are a parallel computing technique used to efficiently perform operations like collective communication and data aggregation among multiple processors. By repeatedly doubling the number of involved processors in each step, these algorithms minimize communication rounds, effectively optimizing performance in distributed systems. They leverage the power of parallelism to achieve faster computation times, making them crucial for scalable applications.
Rendezvous Protocols: Rendezvous protocols are communication mechanisms that enable synchronization between distributed processes by ensuring that they meet at a designated point to exchange information or data. These protocols are critical in parallel computing, especially when processes need to coordinate their actions or share resources effectively, enhancing performance and efficiency.
Replication: Replication refers to the process of creating copies of data or computational tasks to enhance reliability, performance, and availability in distributed and parallel computing environments. It is crucial for fault tolerance, as it ensures that even if one copy fails, others can still provide the necessary data or services. This concept is interconnected with various system architectures and optimization techniques, highlighting the importance of maintaining data integrity and minimizing communication overhead.
Scalasca: Scalasca is a performance analysis tool specifically designed for parallel applications, particularly those using MPI (Message Passing Interface). It helps users understand the performance of their applications by providing insights into communication patterns, computational efficiency, and scalability, which are essential for optimizing parallel and distributed computing environments. The tool offers a variety of features, including profiling, tracing, and visualizations to identify performance bottlenecks and improve overall efficiency.
Smp (symmetric multiprocessing): SMP, or symmetric multiprocessing, refers to a computer architecture where two or more identical processors are connected to a single shared main memory, enabling them to operate simultaneously on tasks. This setup allows for improved performance and efficiency, as multiple processors can work on different threads of a program concurrently. SMP is often used in parallel computing environments, making it particularly relevant when considering advanced performance optimization techniques.
Static Routing: Static routing is a network routing technique where routes are manually configured and fixed by a network administrator. This method allows for predetermined paths for data packets, ensuring consistent delivery without relying on dynamic protocols or algorithms. Static routing is especially beneficial in smaller or simpler networks where traffic patterns are stable, enabling efficient performance optimization and advanced MPI concepts.
Synchronized clocks: Synchronized clocks refer to the practice of ensuring that all clocks in a distributed system reflect the same time, which is crucial for coordination and communication between processes. In parallel and distributed computing, having synchronized clocks helps to maintain consistency in time-dependent operations, data integrity, and the accurate ordering of events across multiple nodes in a network.
Tau: In parallel and distributed computing, tau typically represents the time taken to complete a specific computational task or operation. It is crucial for evaluating the performance of algorithms and systems, helping to understand the impact of different factors such as communication overhead, load balancing, and resource allocation on overall execution time. By analyzing tau, developers can optimize performance and improve efficiency in their applications.
Throughput: Throughput is the measure of how many units of information or tasks can be processed or transmitted in a given amount of time. It is crucial for evaluating the efficiency and performance of various systems, especially in computing environments where multiple processes or data flows occur simultaneously.
Torus network topology: Torus network topology is a type of network arrangement where nodes are connected in a grid pattern, wrapping around both horizontally and vertically to form a continuous loop. This design allows for efficient communication between nodes by minimizing the distance data must travel, which is crucial for high-performance computing systems and parallel processing applications.
Two-Phase I/O: Two-phase I/O is a method used in parallel computing that separates the input and output operations into two distinct phases, optimizing data transfer between the processes and the storage system. This approach helps reduce contention and improve efficiency by allowing processes to gather their data in the first phase before performing the actual input/output operations in the second phase, leading to enhanced performance in distributed systems.
Work stealing: Work stealing is a dynamic load balancing technique used in parallel computing where idle processors 'steal' tasks from busy processors to optimize resource utilization and improve performance. This method helps to mitigate the effects of uneven workload distribution and enhances the overall efficiency of parallel systems.
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