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1. GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU

   https://doi.org/10.1145/3466795  

   Yang, Carl ; Buluç, Aydın ; Owens, John D. ( January 2021 , ACM transactions on mathematical software)

   null (Ed.)

   High-performance implementations of graph algorithms are challenging to implement on new parallel hardware such as GPUs because of three challenges: (1) the difficulty of coming up with graph building blocks, (2) load imbalance on parallel hardware, and (3) graph problems having low arithmetic intensity. To address some of these challenges, GraphBLAS is an innovative, on-going effort by the graph analytics community to propose building blocks based on sparse linear algebra, which allow graph algorithms to be expressed in a performant, succinct, composable, and portable manner. In this paper, we examine the performance challenges of a linear-algebra-based approach to building graph frameworks and describe new design principles for overcoming these bottlenecks. Among the new design principles is exploiting input sparsity, which allows users to write graph algorithms without specifying push and pull direction.Exploiting output sparsityallows users to tell the backend which values of the output in a single vectorized computation they do not want computed. Load-balancing is an important feature for balancing work amongst parallel workers. We describe the important load-balancing features for handling graphs with different characteristics. The design principles described in this paper have been implemented in “GraphBLAST”, the first high-performance linear algebra-based graph framework on NVIDIA GPUs that is open-source. The results show that on a single GPU, GraphBLAST has on average at least an order of magnitude speedup over previous GraphBLAS implementations SuiteSparse andGBTL, comparable performance to the fastest GPU hardwired primitives and shared-memory graph frameworks Ligra and Gunrock, and better performance than any other GPU graph framework ,while offering a simpler and more concise programming model. 

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2. A Programming Model for GPU Load Balancing

   https://doi.org/10.1145/3572848.3577434  

   Osama, Muhammad ; Porumbescu, Serban D. ; Owens, John D. ( February 2023 , Proceedings of the 28th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming)

   We propose a GPU fine-grained load-balancing abstraction that decouples load balancing from work processing and aims to support both static and dynamic schedules with a programmable interface to implement new load-balancing schedules. Prior to our work, the only way to unleash the GPU’s potential on irregular problems has been to workload- balance through application-specific, tightly coupled load- balancing techniques. With our open-source framework for load-balancing, we hope to improve programmers’ productivity when developing irregular-parallel algorithms on the GPU, and also improve the overall performance characteristics for such applications by allowing a quick path to experimentation with a variety of existing load-balancing techniques. Consequently, we also hope that by separating the concerns of load-balancing from work processing within our abstraction, managing and extending existing code to future architectures becomes easier. 

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3. Load‐balanced and locality‐aware scheduling for data‐intensive workloads at extreme scales

   https://doi.org/10.1002/cpe.3617  

   Wang, Ke ; Qiao, Kan ; Sadooghi, Iman ; Zhou, Xiaobing ; Li, Tonglin ; Lang, Michael ; Raicu, Ioan ( August 2015 , Concurrency and Computation: Practice and Experience)

   Data‐driven programming models such as many‐task computing (MTC) have been prevalent for running data‐intensive scientific applications. MTC applies over‐decomposition to enable distributed scheduling. To achieve extreme scalability, MTC proposes a fully distributed task scheduling architecture that employs as many schedulers as the compute nodes to make scheduling decisions. Achieving distributed load balancing and best exploiting data locality are two important goals for the best performance of distributed scheduling of data‐intensive applications. Our previous research proposed a data‐aware work‐stealing technique to optimize both load balancing and data locality by using both dedicated and shared task ready queues in each scheduler. Tasks were organized in queues based on the input data size and location. Distributed key‐value store was applied to manage task metadata. We implemented the technique in MATRIX, a distributed MTC task execution framework. In this work, we devise an analytical suboptimal upper bound of the proposed technique, compare MATRIX with other scheduling systems, and explore the scalability of the technique at extreme scales. Results show that the technique is not only scalable but can achieve performance within 15% of the suboptimal solution. Copyright © 2015 John Wiley & Sons, Ltd.

    

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4. Exploration on Task Scheduling Strategy of CPU-GPU Heterogeneous Computing System

   Juan Fang, Jiaxing Zhang ( January 2022 , 2020 IEEE Computer Society Annual Symposium on VLSI (ISVLSI))

   For a CPU-GPU heterogeneous computing system, different types of processors have load balancing problems in the calculation process. What’s more, multitasking cannot be matched to the appropriate processor core is also an urgent problem to be solved. In this paper, we propose a task scheduling strategy for high-performance CPU-GPU heterogeneous computing platform to solve these problems. For the single task model, a task scheduling strategy based on loadaware for CPU-GPU heterogeneous computing platform is proposed. This strategy detects the computing power of the CPU and GPU to process specified tasks, and allocates computing tasks to the CPU and GPU according to the perception ratio. The tasks are stored in a bidirectional queue to reduce the additional overhead brought by scheduling. For the multi-task model, a task scheduling strategy based on the genetic algorithm for CPU-GPU heterogeneous computing platform is proposed. The strategy aims at improving the overall operating efficiency of the system, and accurately binds the execution relationship between different types of tasks and heterogeneous processing cores. Our experimental results show that the scheduling strategy can improve the efficiency of parallel computing as well as system performance. 

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5. Parla: a Python orchestration system for heterogeneous architectures

   https://doi.org/10.1109/SC41404.2022.00056  

   Lee, Hochan ; Ruys, William ; Henriksen, Ian ; Peters, Arthur ; Yan, Yineng ; Stephens, Sean ; You, Bozhi ; Fingler, Henrique ; Burtscher, Martin ; Gligoric, Milos ; et al ( November 2022 , SC '22: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis)

   Python's ease of use and rich collection of numeric libraries make it an excellent choice for rapidly developing scientific applications. However, composing these libraries to take advantage of complex heterogeneous nodes is still difficult. To simplify writing multi-device code, we created Parla, a heterogeneous task-based programming framework that fully supports Python's scientific programming stack. Parla's API is based on Python decorators and allows users to wrap code in Parla tasks for parallel execution. Parla arrays enable automatic movement of data between devices. The Parla runtime handles resource-aware mapping, scheduling, and execution of tasks. Compared to other Python tasking systems, Parla is unique in its parallelization of tasks within a single process, its GPU context and resource-aware runtime, and its design around gradual adoption to provide easy migration of and integration into existing Python applications. We show that Parla can achieve performance competitive with hand-optimized code while improving ease of development. 

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