112 research outputs found
Heterogeneous computing with an algorithmic skeleton framework
The Graphics Processing Unit (GPU) is present in almost every modern day personal
computer. Despite its specific purpose design, they have been increasingly used for general
computations with very good results. Hence, there is a growing effort from the community
to seamlessly integrate this kind of devices in everyday computing. However, to
fully exploit the potential of a system comprising GPUs and CPUs, these devices should
be presented to the programmer as a single platform.
The efficient combination of the power of CPU and GPU devices is highly dependent
on each device’s characteristics, resulting in platform specific applications that cannot
be ported to different systems. Also, the most efficient work balance among devices is
highly dependable on the computations to be performed and respective data sizes.
In this work, we propose a solution for heterogeneous environments based on the
abstraction level provided by algorithmic skeletons. Our goal is to take full advantage of
the power of all CPU and GPU devices present in a system, without the need for different
kernel implementations nor explicit work-distribution.To that end, we extended Marrow,
an algorithmic skeleton framework for multi-GPUs, to support CPU computations and
efficiently balance the work-load between devices. Our approach is based on an offline
training execution that identifies the ideal work balance and platform configurations for
a given application and input data size.
The evaluation of this work shows that the combination of CPU and GPU devices can
significantly boost the performance of our benchmarks in the tested environments, when
compared to GPU-only executions
Multi-GPU support on the marrow algorithmic skeleton framework
Dissertação para obtenção do Grau de Mestre em
Engenharia InformáticaWith the proliferation of general purpose GPUs, workload parallelization and datatransfer optimization became an increasing concern. The natural evolution from using a single GPU, is multiplying the amount of available processors, presenting new challenges, as tuning the workload decompositions and load balancing, when dealing with heterogeneous systems.
Higher-level programming is a very important asset in a multi-GPU environment, due to the complexity inherent to the currently used GPGPU APIs (OpenCL and CUDA), because of their low-level and code overhead. This can be obtained by introducing an abstraction layer, which has the advantage of enabling implicit optimizations and orchestrations
such as transparent load balancing mechanism and reduced explicit code overhead.
Algorithmic Skeletons, previously used in cluster environments, have recently been
adapted to the GPGPU context. Skeletons abstract most sources of code overhead, by
defining computation patterns of commonly used algorithms. The Marrow algorithmic
skeleton library is one of these, taking advantage of the abstractions to automate the
orchestration needed for an efficient GPU execution.
This thesis proposes the extension of Marrow to leverage the use of algorithmic skeletons
in the modular and efficient programming of multiple heterogeneous GPUs, within a single machine.
We were able to achieve a good balance between simplicity of the programming model and performance, obtaining good scalability when using multiple GPUs, with an efficient load distribution, although at the price of some overhead when using a single-GPU.projects PTDC/EIA-EIA/102579/2008 and PTDC/EIA-EIA/111518/200
On the Virtualization of CUDA Based GPU Remoting on ARM and X86 Machines in the GVirtuS Framework
The astonishing development of diverse and different hardware platforms is twofold: on one side, the challenge for the exascale performance for big data processing and management; on the other side, the mobile and embedded devices for data collection and human machine interaction. This drove to a highly hierarchical evolution of programming models. GVirtuS is the general virtualization system developed in 2009 and firstly introduced in 2010 enabling a completely transparent layer among GPUs and VMs. This paper shows the latest achievements and developments of GVirtuS, now supporting CUDA 6.5, memory management and scheduling. Thanks to the new and improved remoting capabilities, GVirtus now enables GPU sharing among physical and virtual machines based on x86 and ARM CPUs on local workstations, computing clusters and distributed cloud appliances
Toward optimised skeletons for heterogeneous parallel architecture with performance cost model
High performance architectures are increasingly heterogeneous with shared and
distributed memory components, and accelerators like GPUs. Programming such
architectures is complicated and performance portability is a major issue as the
architectures evolve. This thesis explores the potential for algorithmic skeletons
integrating a dynamically parametrised static cost model, to deliver portable
performance for mostly regular data parallel programs on heterogeneous archi-
tectures.
The rst contribution of this thesis is to address the challenges of program-
ming heterogeneous architectures by providing two skeleton-based programming
libraries: i.e. HWSkel for heterogeneous multicore clusters and GPU-HWSkel
that enables GPUs to be exploited as general purpose multi-processor devices.
Both libraries provide heterogeneous data parallel algorithmic skeletons including
hMap, hMapAll, hReduce, hMapReduce, and hMapReduceAll.
The second contribution is the development of cost models for workload dis-
tribution. First, we construct an architectural cost model (CM1) to optimise
overall processing time for HWSkel heterogeneous skeletons on a heterogeneous
system composed of networks of arbitrary numbers of nodes, each with an ar-
bitrary number of cores sharing arbitrary amounts of memory. The cost model
characterises the components of the architecture by the number of cores, clock
speed, and crucially the size of the L2 cache. Second, we extend the HWSkel cost
model (CM1) to account for GPU performance. The extended cost model (CM2)
is used in the GPU-HWSkel library to automatically nd a good distribution
for both a single heterogeneous multicore/GPU node, and clusters of heteroge-
neous multicore/GPU nodes. Experiments are carried out on three heterogeneous
multicore clusters, four heterogeneous multicore/GPU clusters, and three single
heterogeneous multicore/GPU nodes. The results of experimental evaluations for
four data parallel benchmarks, i.e. sumEuler, Image matching, Fibonacci, and
Matrix Multiplication, show that our combined heterogeneous skeletons and cost
models can make good use of resources in heterogeneous systems. Moreover using
cores together with a GPU in the same host can deliver good performance either
on a single node or on multiple node architectures
Autonomic behavioural framework for structural parallelism over heterogeneous multi-core systems.
With the continuous advancement in hardware technologies, significant research has been devoted to design and develop high-level parallel programming models that allow programmers to exploit the latest developments in heterogeneous multi-core/many-core architectures. Structural programming paradigms propose a viable solution for e ciently programming modern heterogeneous multi-core architectures equipped with one or more programmable Graphics Processing Units (GPUs). Applying structured programming paradigms, it is possible to subdivide a system into building blocks (modules, skids or components) that can be independently created and then used in di erent systems to derive multiple functionalities. Exploiting such systematic divisions, it is possible to address extra-functional features such as application performance, portability and resource utilisations from the component level in heterogeneous multi-core architecture. While the computing function of a building block can vary for di erent applications, the behaviour (semantic) of the block remains intact. Therefore, by understanding the behaviour of building blocks and their structural compositions in parallel patterns, the process of constructing and coordinating a structured application can be automated. In this thesis we have proposed Structural Composition and Interaction Protocol (SKIP) as a systematic methodology to exploit the structural programming paradigm (Building block approach in this case) for constructing a structured application and extracting/injecting information from/to the structured application. Using SKIP methodology, we have designed and developed Performance Enhancement Infrastructure (PEI) as a SKIP compliant autonomic behavioural framework to automatically coordinate structured parallel applications based on the extracted extra-functional properties related to the parallel computation patterns. We have used 15 di erent PEI-based applications (from large scale applications with heavy input workload that take hours to execute to small-scale applications which take seconds to execute) to evaluate PEI in terms of overhead and performance improvements. The experiments have been carried out on 3 di erent Heterogeneous (CPU/GPU) multi-core architectures (including one cluster machine with 4 symmetric nodes with one GPU per node and 2 single machines with one GPU per machine). Our results demonstrate that with less than 3% overhead, we can achieve up to one order of magnitude speed-up when using PEI for enhancing application performance
Mapping parallel programs to heterogeneous multi-core systems
Heterogeneous computer systems are ubiquitous in all areas of computing, from mobile
to high-performance computing. They promise to deliver increased performance
at lower energy cost than purely homogeneous, CPU-based systems. In recent years
GPU-based heterogeneous systems have become increasingly popular. They combine
a programmable GPU with a multi-core CPU. GPUs have become flexible enough
to not only handle graphics workloads but also various kinds of general-purpose
algorithms. They are thus used as a coprocessor or accelerator alongside the CPU.
Developing applications for GPU-based heterogeneous systems involves several
challenges. Firstly, not all algorithms are equally suited for GPU computing. It is thus
important to carefully map the tasks of an application to the most suitable processor
in a system. Secondly, current frameworks for heterogeneous computing, such as
OpenCL, are low-level, requiring a thorough understanding of the hardware by the
programmer. This high barrier to entry could be lowered by automatically generating
and tuning this code from a high-level and thus more user-friendly programming
language. Both challenges are addressed in this thesis.
For the task mapping problem a machine learning-based approach is presented in
this thesis. It combines static features of the program code with runtime information
on input sizes to predict the optimal mapping of OpenCL kernels. This approach is
further extended to also take contention on the GPU into account. Both methods are
able to outperform competing mapping approaches by a significant margin.
Furthermore, this thesis develops a method for targeting GPU-based heterogeneous
systems from OpenMP, a directive-based framework for parallel computing.
OpenMP programs are translated to OpenCL and optimized for GPU performance.
At runtime a predictive model decides whether to execute the original OpenMP code
on the CPU or the generated OpenCL code on the GPU. This approach is shown to
outperform both a competing approach as well as hand-tuned code
Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016)
Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.The PhD Symposium was a very good opportunity for the young researchers to share information and knowledge, to
present their current research, and to discuss topics with other students in order to look for synergies and common research
topics. The idea was very successful and the assessment made by the PhD Student was very good. It also helped to
achieve one of the major goals of the NESUS Action: to establish an open European research network targeting sustainable
solutions for ultrascale computing aiming at cross fertilization among HPC, large scale distributed systems, and big
data management, training, contributing to glue disparate researchers working across different areas and provide a meeting
ground for researchers in these separate areas to exchange ideas, to identify synergies, and to pursue common activities in
research topics such as sustainable software solutions (applications and system software stack), data management, energy
efficiency, and resilience.European Cooperation in Science and Technology. COS
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