Execution of compound multi-kernel OpenCL computations in multi-CPU/multi-GPU environments (original) (raw)
Related papers
Current computational systems are heterogeneous by nature, featuring a combination of CPUs and GPUs. As the latter are becoming an established platform for high-performance computing, the focus is shifting towards the seamless programming of the heterogeneous systems as a whole. The distinct nature of the architectural and execution models in place raise several challenges, as the best hardware configuration is behavior and data-set dependent. In this paper, we focus the execution of compound computations in multi-CPU/multi-GPU environments, in the scope of Marrow algorithmic skeleton framework, the only, to the best of our knowledge, to support skeleton nesting in GPU computing. We address how these computations may be efficiently scheduled onto the target hardware, and how the system may adapt itself to changes in the CPU's load and in the input data-set.
Toward OpenCL Automatic Multi-Device Support
To fully tap into the potential of today heterogeneous machines, offloading parts of an application on accelerators is no longer sufficient. The real challenge is to build systems where the application would permanently spread across the entire machine, that is, where parallel tasks would be dynamically scheduled over the full set of available processing units. In this paper we present SOCL, an OpenCL implementation that improves and simplifies the programming experience on heterogeneous architectures. SOCL enables applications to dynamically dispatch computation kernels over processing devices so as to maximize their utilization. OpenCL applications can incrementally make use of light extensions to automatically schedule kernels in a controlled manner on multi-device architectures. We demonstrate the relevance of our approach by experimenting with several OpenCL applications on a range of heterogeneous architectures. We show that performance portability is enhanced by using SOCL extensions.
Exploiting Task Parallelism with OpenCL: A Case Study
Journal of Signal Processing Systems
While data parallelism aspects of OpenCL have been of primary interest due to the massively data parallel GPUs being on focus, OpenCL also provides powerful capabilities to describe task parallelism. In this article we study the task parallel concepts available in OpenCL and find out how well the different vendor-specific implementations can exploit task parallelism when the parallelism is described in various ways utilizing the command queues. We show that the vendor implementations are not yet capable of extracting kernel-level task parallelism from in-order queues automatically. To assess the potential performance benefits of in-order queue parallelization, we implemented such capabilities to an open source implementation of OpenCL. The evaluation was conducted by means of a case study of an advanced noise reduction algorithm described as a multi-kernel OpenCL application.
An OpenCL framework for heterogeneous multicores with local memory
2010
In this paper, we present the design and implementation of an Open Computing Language (OpenCL) framework that targets heterogeneous accelerator multicore architectures with local memory. The architecture consists of a general-purpose processor core and multiple accelerator cores that typically do not have any cache. Each accelerator core, instead, has a small internal local memory. Our OpenCL runtime is based on software-managed caches and coherence protocols that guarantee OpenCL memory consistency to overcome the limited size of the local memory. To boost performance, the runtime relies on three source-code transformation techniques, work-item coalescing, web-based variable expansion and preload-poststore buffering, performed by our OpenCL C source-to-source translator. Work-item coalescing is a procedure to serialize multiple SPMD-like tasks that execute concurrently in the presence of barriers and to sequentially run them on a single accelerator core. It requires the webbased variable expansion technique to allocate local memory for private variables. Preload-poststore buffering is a buffering technique that eliminates the overhead of software cache accesses. Together with work-item coalescing, it has a synergistic effect on boosting performance. We show the effectiveness of our OpenCL framework, evaluating its performance with a system that consists of two Cell BE processors. The experimental result shows that our approach is promising.
Auto-tuned OpenCL kernel co-execution in OmpSs for heterogeneous systems
Journal of Parallel and Distributed Computing, 2018
The emergence of heterogeneous systems has been very notable recently. Still their programming is a complex task. The co-execution of a single OpenCL kernel on several devices is a challenging endeavour, requiring considering the different computing capabilities of the devices and application behaviour. OmpSs is a framework for task based parallel applications, that does not support coexecution between several devices. This paper presents an extension of OmpSs that solves two main issues. First, the automatic distribution of datasets and the management of device memory address spaces. Second, the implementation of a set of load balancing algorithms to adapt to the particularities of applications and systems. All this is accomplished with negligible impact on the programming. Experimental results reveal that the use of all the devices in the system is beneficial in terms performance and energy consumption. Also, the Auto-Tune algorithm gives the best overall results without requiring manual parameter tuning.
OpenCL Performance Evaluation on Modern Multi Core CPUs
2013 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum, 2013
Utilizing heterogeneous platforms for computation has become a general trend, making the portability issue important. OpenCL (Open Computing Language) serves this purpose by enabling portable execution on heterogeneous architectures. However, unpredictable performance variation on different platforms has become a burden for programmers who write OpenCL applications. This is especially true for conventional multicore CPUs, since the performance of general OpenCL applications on CPUs lags behind the performance of their counterparts written in the conventional parallel programming model for CPUs. In this paper, we evaluate the performance of OpenCL applications on out-of-order multicore CPUs from the architectural perspective. We evaluate OpenCL applications on various aspects, including API overhead, scheduling overhead, instruction-level parallelism, address space, data location, data locality, and vectorization, comparing OpenCL to conventional parallel programming models for CPUs. Our evaluation indicates unique performance characteristics of OpenCL applications and also provides insight into the optimization metrics for better performance on CPUs.
Improving CPU Performance For Heterogeneous Computing Using OpenCL
International journal of engineering research and technology, 2013
With the rapid development of smart phones, tablets, and pads, there has been widespread adoption of Graphic Processing Units (GPUs). The hand-held market is now seeing an ever-increasing rate of development of computationally intensive applications, which require significant amounts of processing resources. To meet this challenge, GPUs can be used for general-purpose processing. We are moving towards a future where devices will be more connected and integrated. This will allow applications to run on handheld devices, while offloading computationally intensive tasks to other compute units available. There is a growing need for a general programming framework which can utilize heterogeneous processing units such as GPUs and DSPs. OpenCL, a widely used programming framework has been a step towards integrating these different processing units on desktop platforms. A thorough study of the factors that impact the performance of OpenCL on CPUs. Specifically, starting from the two main arc...
When adapting GPU-specific OpenCL kernels to run on multi-core/many-core CPUs, coarsening the thread granularity is necessary and thus extensively used. However, locality concerns exposed in GPU-specific OpenCL code are usually inherited without analysis, which may give side-effects on the CPU performance. When executing GPU-specific kernels on CPUs, local-memory arrays no longer match well with the hardware and the associated synchronizations are costly. To solve this dilemma, we actively analyze the memory access patterns by using array-access descriptors derived from GPU-specific kernels, which can thus be adapted for CPUs by removing all the unwanted local-memory arrays together with the obsolete barrier statements. Experiments show that the automated transformation can satisfactorily improve OpenCL kernel performances on Sandy Bridge CPU and Intel's Many-Integrated-Core coprocessor.
A load balance multi-scheduling model for OpenCL kernel tasks in an integrated cluster
Soft Computing, 2020
Nowadays, embedded systems are comprised of heterogeneous multi-core architectures, i.e., CPUs and GPUs. If the application is mapped to an appropriate processing core, then these architectures provide many performance benefits to applications. Typically, programmers map sequential applications to CPU and parallel applications to GPU. The task mapping becomes challenging because of the usage of evolving and complex CPU- and GPU-based architectures. This paper presents an approach to map the OpenCL application to heterogeneous multi-core architecture by determining the application suitability and processing capability. The classification is achieved by developing a machine learning-based device suitability classifier that predicts which processor has the highest computational compatibility to run OpenCL applications. In this paper, 20 distinct features are proposed that are extracted by using the developed LLVM-based static analyzer. In order to select the best subset of features, fe...