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Design Time characterisation for FPGA offloaded computations using Poroto

Lotfi Guedria's picture Submitted by Lotfi Guedria on

In the context of development process for a software application targetting heterogenous architectures, Design Time Characterisation is a step that consists of making rapid prototyping and early performance evaluation of selected parts of computations, which helps making  better and more objective design choices for the implementation.

This blog post is a quick introduction of Poroto : a DTC (Design Time Characterisation) tool from the TANGO toolchain.

Productivity when programming parallel application for heterogeneous platforms

Jorge Ejarque's picture Submitted by Jorge Ejarque on

Recent trends in computing science that aim to improve computation performance are based on incorporating different types of computing devices such as GPUs, FPGAs and CPU architectures which are specialized for accelerating different type of algorithms or reducing the energy consumption. Moreover, with the rise of Cloud and Fog computing, these devices can be distributed geographically and connected through the internet by means of different types of networks.

Secure Distributed Computation in TANGO

Submitted by James Dyer on

Implementing a security framework for the TANGO project is a daunting task, especially considering that any proposed architecture should impact the ongoing development on the project as little as possible. In this blog post, we intend to cover one aspect of security in TANGO: securing application data as it is processed in TANGO. If this data is sensitive, then there may be legal or regulatory requirements to ensure that such data is not revealed to unauthorised individuals.

Monitoring heterogeneous processors with collectd - how to create a custom plugin in C

Submitted by RoiSucasas on

In this blog post we will walk through the process of creating a collectd plugin in C for monitoring NVIDIA GPUs.

In a previous blog we introduced the monitoring objectives of the TANGO solution. One of these objectives is the monitoring of different kind of processors, including NVIDIA GPUs, in order to get from them energy measurements, among other metrics. For this purpose, we decided to rely on collectd and integrate it in the stack of tools responsible for the monitoring tasks. Collectd is a UNIX daemon, written in C, which periodically collects system and applications performance metrics. One of the main advantages of this tool is that it uses a modular approach which allows the use of custom plugins to enhance its monitoring capabilities.

MPMD model for HPC heterogeneous devices

Maria Carbonell's picture Submitted by Maria Carbonell on

General applications used in HPC are based on SPMD models – SPMD meaning Simple Program-Multiple Data. These applications are the one we usually understand well for HPC. The same binary (program) is started on several HPC devices but use different input (data). The devices are defined as processors including or defining as threads, and enhanced by accelerators such as GPU (graphics processing unit) or other specialized processing unit. This arcticle shows the potential of Device Supervisor of TANGO for MPMD models. Below you can find attached the entire document.

Simplifying heterogeneity programming with OpenCL targets using the OmpSs Programming Model

Maria Carbonell's picture Submitted by Maria Carbonell on

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!

Maria Carbonell's picture Submitted by Maria Carbonell on

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

Jorge Ejarque's picture Submitted by Jorge Ejarque on

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

David Garcia's picture Submitted by David Garcia on

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

Jean-Christophe Deprez's picture Submitted by Jean-Christophe... on

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.

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