34,050 research outputs found
Heterogeneous programming using OpenMP and CUDA/HIP for hybrid CPU-GPU scientific applications
Hybrid computer systems combine compute units (CUs) of different nature like CPUs, GPUs and FPGAs. Simultaneously exploiting the computing power of these CUs requires a careful decomposition of the applications into balanced parallel tasks according to both the performance of each CU type and the communication costs among them. This paper describes the design and implementation of runtime support for OpenMP hybrid GPU-CPU applications, when mixed with GPU-oriented programming models (e.g. CUDA/HIP). The paper describes the case for a hybrid multi-level parallelization of the NPB-MZ benchmark suite. The implementation exploits both coarse-grain and fine-grain parallelism, mapped to compute units of different nature (GPUs and CPUs). The paper describes the implementation of runtime support to bridge OpenMP and HIP, introducing the abstractions of Computing Unit and Data Placement. We compare hybrid and non-hybrid executions under state-of-the-art schedulers for OpenMP: static and dynamic task schedulings. Then, we improve the set of schedulers with two additional variants: a memorizing-dynamic task scheduling and a profile-based static task scheduling. On a computing node composed of one AMD EPYC 7742 @ 2.250 GHz (64 cores and 2 threads/core, totalling 128 threads per node) and 2 Ă— GPU AMD Radeon Instinct MI50 with 32 GB, hybrid executions present speedups from 1.10Ă— up to 3.5Ă— with respect to a non-hybrid GPU implementation, depending on the number of activated CUs.This work was supported by the Spanish Ministry of Science and Technology (PID2019-107255GB).Peer ReviewedPostprint (author's final draft
Hybrid static/dynamic scheduling for already optimized dense matrix factorization
We present the use of a hybrid static/dynamic scheduling strategy of the task
dependency graph for direct methods used in dense numerical linear algebra.
This strategy provides a balance of data locality, load balance, and low
dequeue overhead. We show that the usage of this scheduling in communication
avoiding dense factorization leads to significant performance gains. On a 48
core AMD Opteron NUMA machine, our experiments show that we can achieve up to
64% improvement over a version of CALU that uses fully dynamic scheduling, and
up to 30% improvement over the version of CALU that uses fully static
scheduling. On a 16-core Intel Xeon machine, our hybrid static/dynamic
scheduling approach is up to 8% faster than the version of CALU that uses a
fully static scheduling or fully dynamic scheduling. Our algorithm leads to
speedups over the corresponding routines for computing LU factorization in well
known libraries. On the 48 core AMD NUMA machine, our best implementation is up
to 110% faster than MKL, while on the 16 core Intel Xeon machine, it is up to
82% faster than MKL. Our approach also shows significant speedups compared with
PLASMA on both of these systems
Hierarchical Dynamic Loop Self-Scheduling on Distributed-Memory Systems Using an MPI+MPI Approach
Computationally-intensive loops are the primary source of parallelism in
scientific applications. Such loops are often irregular and a balanced
execution of their loop iterations is critical for achieving high performance.
However, several factors may lead to an imbalanced load execution, such as
problem characteristics, algorithmic, and systemic variations. Dynamic loop
self-scheduling (DLS) techniques are devised to mitigate these factors, and
consequently, improve application performance. On distributed-memory systems,
DLS techniques can be implemented using a hierarchical master-worker execution
model and are, therefore, called hierarchical DLS techniques. These techniques
self-schedule loop iterations at two levels of hardware parallelism: across and
within compute nodes. Hybrid programming approaches that combine the message
passing interface (MPI) with open multi-processing (OpenMP) dominate the
implementation of hierarchical DLS techniques. The MPI-3 standard includes the
feature of sharing memory regions among MPI processes. This feature introduced
the MPI+MPI approach that simplifies the implementation of parallel scientific
applications. The present work designs and implements hierarchical DLS
techniques by exploiting the MPI+MPI approach. Four well-known DLS techniques
are considered in the evaluation proposed herein. The results indicate certain
performance advantages of the proposed approach compared to the hybrid
MPI+OpenMP approach
A Novel Workload Allocation Strategy for Batch Jobs
The distribution of computational tasks across a diverse set of geographically distributed heterogeneous resources is a critical issue in the realisation of true computational grids. Conventionally, workload allocation algorithms are divided into static and dynamic approaches. Whilst dynamic approaches frequently outperform static schemes, they usually require the collection and processing of detailed system information at frequent intervals - a task that can be both time consuming and unreliable in the real-world. This paper introduces a novel workload allocation algorithm for optimally distributing the workload produced by the arrival of batches of jobs. Results show that, for the arrival of batches of jobs, this workload allocation algorithm outperforms other commonly used algorithms in the static case. A hybrid scheduling approach (using this workload allocation algorithm), where information about the speed of computational resources is inferred from previously completed jobs, is then introduced and the efficiency of this approach demonstrated using a real world computational grid. These results are compared to the same workload allocation algorithm used in the static case and it can be seen that this hybrid approach comprehensively outperforms the static approach
Task scheduling techniques for asymmetric multi-core systems
As performance and energy efficiency have become the main challenges for next-generation high-performance computing, asymmetric multi-core architectures can provide solutions to tackle these issues. Parallel programming models need to be able to suit the needs of such systems and keep on increasing the application’s portability and efficiency. This paper proposes two task scheduling approaches that target asymmetric systems. These dynamic scheduling policies reduce total execution time either by detecting the longest or the critical path of the dynamic task dependency graph of the application, or by finding the earliest executor of a task. They use dynamic scheduling and information discoverable during execution, fact that makes them implementable and functional without the need of off-line profiling. In our evaluation we compare these scheduling approaches with two existing state-of the art heterogeneous schedulers and we track their improvement over a FIFO baseline scheduler. We show that the heterogeneous schedulers improve the baseline by up to 1.45 in a real 8-core asymmetric system and up to 2.1 in a simulated 32-core asymmetric chip.This work has been supported by the Spanish Government (SEV2015-0493), by the Spanish Ministry of Science and Innovation (contract TIN2015-65316-P), by Generalitat de
Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272), by the RoMoL ERC Advanced Grant (GA 321253) and the
European HiPEAC Network of Excellence. The Mont-Blanc project receives funding from the EU’s Seventh Framework Programme (FP7/2007-2013) under grant agreement
no 610402 and from the EU’s H2020 Framework Programme (H2020/2014-2020) under grant agreement no 671697. M.
MoretĂł has been partially supported by the Ministry of Economy and Competitiveness under Juan de la Cierva postdoctoral fellowship number JCI-2012-15047. M. Casas
is supported by the Secretary for Universities and Research of the Ministry of Economy and Knowledge of the Government of Catalonia and the Cofund programme of the Marie
Curie Actions of the 7th R&D Framework Programme of the European Union (Contract 2013 BP B 00243).Peer ReviewedPostprint (author's final draft
A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing
The emergence of cloud computing based on virtualization technologies brings
huge opportunities to host virtual resource at low cost without the need of
owning any infrastructure. Virtualization technologies enable users to acquire,
configure and be charged on pay-per-use basis. However, Cloud data centers
mostly comprise heterogeneous commodity servers hosting multiple virtual
machines (VMs) with potential various specifications and fluctuating resource
usages, which may cause imbalanced resource utilization within servers that may
lead to performance degradation and service level agreements (SLAs) violations.
To achieve efficient scheduling, these challenges should be addressed and
solved by using load balancing strategies, which have been proved to be NP-hard
problem. From multiple perspectives, this work identifies the challenges and
analyzes existing algorithms for allocating VMs to PMs in infrastructure
Clouds, especially focuses on load balancing. A detailed classification
targeting load balancing algorithms for VM placement in cloud data centers is
investigated and the surveyed algorithms are classified according to the
classification. The goal of this paper is to provide a comprehensive and
comparative understanding of existing literature and aid researchers by
providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres
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