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Simplifying heterogeneity programming with OpenCL targets using the OmpSs Programming Model

OpenCL is now one of the preferred “non-proprietary” way for programming the “acceleration”
part of a heterogeneous platform. OpenCL is supported by a large panel of manufacturers, enabling it
for programming on various GPUs, including SoCs accelerators but also FPGAs.
OpenCL enables writing computing kernels with a C-like language in a very simple way.
But a drawback of OpenCL is the code that is required to access these kernels from the CPU. Provided
APIs propose “buffer oriented” data transfers, and execution control requires multiple statements.

TANGO Development-time tooling: An initial usage of Placer!

This blog article presents how Placer can be used on a real-world use case. In particular, it provides
information to an embedded application developer for exploring quickly the optimal placement and
schedule of the tasks computed by an embedded application named AquaScan. Alternatively, Placer
can also handle additional placement constraints to let the developer study the impact on time
performance in case the optimal placement and schedule are not followed.

Impact of the TANGO Programming Model Adaptation Features in the Execution Quality

In previous blog entries, we presented the overview of the TANGO Programming Model (PM) and Runtime as a hierarchical integration of the COMPSs and OmpSs task-based programming models, and how it can facilitate the programming of parallel applications in distributed heterogeneous platforms. We have also commented some energy considerations when programming applications with the TANGO PM.

Containers in HPC

In the last 10 years, the IT world has suffered the virtualization and Cloud revolution. It started at the beginning with Virtual Machines (VMs) and nowadays the tendency it is to move to Container-based execute applications. The former version, the VM one, has not impact in the High-Performance Computing (HPC) world, but with containers, we can see a significant effort to start using it on supercomputers. To see why, let’s start first with the differences between VMs and Containers.

Virtual Machines:

TANGO Development-time tooling: Leveraging a Framework from Operations Research for optimal software placement and task scheduling on heterogeneous hardware platforms

In the emerging era of Internet of Things and Big Data processing, the paradigm of software development is shifting. In particular, many IoT and Big Data problems can be solved using vector and matrix operations and these operations can be drastically accelerated by using massively parallel processing units such as GPU and FPGA, which are now integrated in many recent heterogeneous platforms found on the market.

Energy considerations with the TANGO Programming Model and Runtime

One of the recent trending topics in computer science research is the reduction of energy and power consumed by computers. Part of this research is focused on providing low power architectures and accelerators which can achieve good performance at low power. However, an application should be modified to take advantage of this heterogeneous architecture.

Characterizing your application power profile using TANGO Device Supervisor

TANGO project aims to deliver the best way to run application with low power consumption. TANGO helps defining the right hardware (CPU, GPU, FPGA …) according to the application needs. The Device Supervisor (defined in the global TANGO architecture) is in charge of managing the available resources from their allocation to their usage. One of the key parameter to follow is the energy consumption.

TANGO for the Embedded World

Two decades ago, parallel programming was a technology restricted to large scale specialised applications that were running in computing centres. Today’s picture is very different with the generalisation of multi-core processing devices, not only inside desktop computers, but inside embedded and mobile devices as well.

Image processing, The Internet of Things (IoT) and cloud computing are examples of domains in which concurrent cooperative programming, that-is-to-say parallel programming, develops widely.

TANGO Development-time tooling: Guiding software design decision for exploiting heterogeneous hardware platforms’ capabilities

Designing and developing software with an efficient execution on distributed environment with fairly standard homogeneous processing nodes is already a difficult exercise. This complexity explodes when targeting a heterogeneous environment composed not only of distributed multi-core CPU nodes but also including accelerators with many-core CPUs, GPUs and FPGAs.

Quality of Monitoring Data with IPMI and RAPL

One of the main objectives of the TANGO Project is to be able to optimize energy usage of applications in a HPC environment, including the prospect of handling heterogeneity such as CPUs, GPUs and FPGAs.

A key start to optimisation is the ability to monitor such infrastructures and to ensure the data obtained is accurate and consistent. The accuracy of this data is particularly useful when constructing power models that can be used to estimate future power consumption based upon expected utilisation. In this blog we provide key recommendations and findings from performing calibration on our infrastructure.

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