rCUDA: Reducing the number of GPU-based accelerators in high performance clusters

Duato, J.; Peña, Antonio J.; Silla, Federico; Mayo, Rafael; Quintana–Ort́ı, Enrique S.

doi:10.1109/hpcs.2010.5547126

Cited by 219 publications

(109 citation statements)

References 12 publications

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“…In this case, the application does not need to be modified at all. Accessing remote Nvdia GPU through rCUDA [33] has been demonstrated, and has been shown to actually outperform locally connected GPU when there is appropriate network connectivity.…”

Section: Software Stackmentioning

confidence: 99%

Composable architecture for rack scale big data computing

Franke

Parris

et al. 2017

Future Generation Computer Systems

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Keywords:Big data platforms, Composable system architecture, Disaggregated datacenter architecture, composable datacenter, software defined environments, software defined networking. Abstract:The rapid growth of cloud computing, both in terms of the spectrum and volume of cloud workloads, necessitate re-visiting the traditional rack-mountable servers based datacenter design. Next generation datacenters need to offer enhanced support for: (i) fast changing system configuration requirements due to workload constraints, (ii) timely adoption of emerging hardware technologies, and (iii) maximal sharing of systems and subsystems in order to lower costs. Disaggregated datacenters, constructed as a collection of individual resources such as CPU, memory, disks etc., and composed into workload execution units on demand, are an interesting new trend that can address the above challenges. In this paper, we demonstrated the feasibility of composable systems through building a rack scale composable system prototype using PCIe switch. Through empirical approaches, we develop assessment of the opportunities and challenges for leveraging the composable architecture for rack scale cloud datacenters with a focus on big data and NoSQL workloads. In particular, we compare and contrast the programming models that can be used to access the composable resources, and developed the implications for the network and resource provisioning and management for rack scale architecture.

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Section: Software Stackmentioning

confidence: 99%

Composable architecture for rack scale big data computing

Franke

Parris

et al. 2017

Future Generation Computer Systems

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“…rCUDA [11] provides this functionality for Nvidia devices and Mosix VCL [12] provides this functionality for OpenCL on Linux-based systems. Hybrid OpenCL [13], dOpenCL [14], and Distributed OpenCL [15] provide this functionality cross vendor and cross platform.…”

Section: E Snuclmentioning

confidence: 99%

DistCL: A Framework for the Distributed Execution of OpenCL Kernels

Diop

Gurfinkel

Anderson

et al. 2013

2013 IEEE 21st International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems

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Abstract-GPUs are used to speed up many scientific computations; however, to use several networked GPUs concurrently, the programmer must explicitly partition work and transmit data between devices. We propose DistCL, a novel framework that distributes the execution of OpenCL kernels across a GPU cluster. DistCL makes multiple distributed compute devices appear to be a single compute device. DistCL abstracts and manages many of the challenges associated with distributing a kernel across multiple devices including: (1) partitioning work into smaller parts, (2) scheduling these parts across the network, (3) partitioning memory so that each part of memory is written to by at most one device, and (4) tracking and transferring these parts of memory. Converting an OpenCL application to DistCL is straightforward and requires little programmer effort. This makes it a powerful and valuable tool for exploring the distributed execution of OpenCL kernels. We compare DistCL to SnuCL, which also facilitates the distribution of OpenCL kernels. We also give some insights: distributed tasks favor more compute bound problems and favour large contiguous memory accesses. DistCL achieves a maximum speedup of 29.1 and average speedups of 7.3 when distributing kernels among 32 peers over an Infiniband cluster.

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“…With rCUDA [11], applications may use CUDA-compatible GPUs installed in remote computers as if they were local. rCUDA follows the client-server model: clients use a wrapper library and a GPU network service listens for TCP requests.…”

Section: Related Workmentioning

confidence: 99%

clOpenCL - Supporting Distributed Heterogeneous Computing in HPC Clusters

Alves

Rufino

Pina

et al. 2013

Lecture Notes in Computer Science

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Abstract. Clusters that combine heterogeneous compute device architectures, coupled with novel programming models, have created a true alternative to traditional (homogeneous) cluster computing, allowing to leverage the performance of parallel applications. In this paper we introduce clOpenCL, a platform that supports the simple deployment and efficient running of OpenCL-based parallel applications that may span several cluster nodes, expanding the original single-node OpenCL model. clOpenCL is deployed through user level services, thus allowing OpenCL applications from different users to share the same cluster nodes and their compute devices. Data exchanges between distributed clOpenCL components rely on Open-MX, a high-performance communication library. We also present extensive experimental data and key conditions that must be addressed when exploiting clOpenCL with real applications.

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rCUDA: Reducing the number of GPU-based accelerators in high performance clusters

Cited by 219 publications

References 12 publications

Composable architecture for rack scale big data computing

Composable architecture for rack scale big data computing

DistCL: A Framework for the Distributed Execution of OpenCL Kernels

clOpenCL - Supporting Distributed Heterogeneous Computing in HPC Clusters

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