Software & Apps

Showing 1 - 20 results of 23

ALOJA Big Data Benchmarking platform: includes tools to define and deploy clusters, orchestrate benchmarking, collect and manage results, and analyze them in Web app including Predictive Analytic tools 

Alya is a high performance computational mechanics code to solve engineering coupled problems.

Autosubmit: a versatile tool to manage Weather and Climate Experiments in diverse Supercomputing Environments.

The performance tools developed at BSC are an open-source project targeting not only to detect performance problems but to understand the applications' behavior.

Barcelona Subsurface Imaging Tools (BSIT) is a software platform, designed and developed to fulfill the geophysical exploration needs for HPC applications.

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COMP Superscalar (COMPSs) is a framework which aims to ease the development and execution of applications for distributed infrastructures, such as Clusters, Grids and Clouds.

DLB is a library devoted to speedup hybrid parallel applications. And at the same time DLB improves the efficient use of the computational resources inside a computing node.
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Tool for the estimation of probabilistic WCET based on execution time measurements (in the form of an R script). 

Details of the method available in:

Mercurium is a source-to-source compilation infrastructure aimed at fast prototyping. Current supported languages are C99, C++11 and Fortran 95. Mercurium is mainly used along with the Nanos++ runtime to implement projects for OmpSs and OpenMP but since it is quite extensible it has been used in other projects including (but not limiting to) Cell Superscalar, ACOTES, software transactional memory, vectorization and correctness.

Multi-cores in real-time systems: opportunities and challenges
Multi-core processors are becoming the baseline computing solution in critical embedded systems. While multi-cores allow high software integration levels, hence reducing hardware procurement and SWaP (Space, Weight and Power) costs, their use challenge current practices in timing analysis.

Nanos++ is a runtime designed to serve as runtime support in parallel environments. It is mainly used to support  OmpSs, a extension to OpenMP developed at BSC. It also has modules to support  OpenMP 3.1.

PARSECSs is a suite of benchmark applications for parallel architectures.  PARSECSs expands the original PARSEC suite with task-based implementations using the OmpSs and/or OpenMP 4.0 programming models.  The implementation make use of concepts such as task-parallelism and dataflow relations to achieve maximum performance and offer a diverse set of applications from a wide range of domains.  It is designed to use broad concepts of task-parallelism in order to make porting to any generic task-based model easy, and offer important insight to the HPC community in regards to the efficiency and programmability of such models.

PETGEM is an HPC python code for the simulation of electromagnetic fields in real 3D CSEM FM that arise in the geophysics context.


The PMES Framework allows users to execute jobs in the cloud.

pyDock is a fast protocol which uses electrostatics and desolvation energy to score docking poses generated with FFT-based algorithms.

Saiph is a Domain Specific Language developed at BSC for simulating physical phenomena modeled by Partial Differential Equations systems designed for users  that are not experts in numerical methods neither programming for supercomputers

servIoTicy is a scalable IoT stream processing platform. It provides multi-tenant data stream processing capabilities, a REST API, data analytics, advanced queries and multi-protocol support in a combination of advanced data-centric services.

dataClay is a distributed data store that enables applications to store and access objects in the same format they have in memory, and executes object methods within the data store. These two main features accelerate both the development of applications and their execution.

Energy Aware Runtime (EAR) offers an automatic, dynamic and transparent solution to energy aware users with minimal performance overhead. EAR  dynamically detects the iterative pattern of an HPC application while it’s running (through Dynamic Pattern Detection - DPD). Once the application structure is detected, it monitors the application and computes the application signature (CPI,GBs,Time and Power) and selects the optimal node frequency based on user-guided energy policies to quantify the trade-off between power, performance and energy. These policies use performance models that receive as input the application signature, computed at runtime,  and the architecture characterization done at installation time.

Volcanic ash dispersal modelling