IPM HPC User portal gives the user the most amazing experience working with high-end HPC platforms ( Multi GPU/CPU ) in the easiest manner. Our user-friendly web-application supports the wide range of HPC applications and is easy to be used by begginers, on the other hand gives expert users full configuration options. To register go to the portal tab. HPC Lab at IPM gives solutions for Building High Performance Systems. Our expert team are available for Consultant Services. IPM-HPC also provide woeld calss solutions for High Performance Problems using MPI Programming / GPU programming / FPGA programming etc.
The main services provided by the center are as follows:
— CPU & GPU Clusters
— CPU & GPU Virtual Machines
— CPU & GPU Colaborative Computing
CPU & GPU Clusters
Cluster computing is a type of computing architecture in which a group of interconnected computers work together as a single system to perform high-performance computing tasks. In a cluster, each computer, or node, is responsible for executing a portion of the workload, and they communicate with each other to coordinate the overall processing.
Scheduling plays a critical role in optimizing the performance and efficiency of cluster computing systems. Scheduling is the process of managing and allocating resources within a cluster to optimize its performance and efficiency. This involves determining which nodes should be used for which tasks, as well as when and how long they should be used. Cluster schedulers typically use algorithms to make these decisions based on factors such as the workload, the available resources, and the priorities of different jobs.
Cluster computing is commonly used in scientific research, data analysis, and other applications that require large amounts of processing power. It can also be used for high availability and fault tolerance, as the failure of one node does not necessarily cause the entire system to fail.
CPU & GPU High-Perfomance Virtual Machines
High-performance virtual machines (VMs) are specialized types of virtual machines that are designed to provide high levels of computing power for demanding workloads. These VMs are typically optimized for specific types of workloads, such as processing-intensive workloads that require a lot of CPU power, or workloads that require high levels of graphical computing power, such as machine learning, deep learning, and AI workloads.
High-performance VMs typically have more powerful hardware configurations compared to standard VMs, including faster CPUs, more memory, and higher-performance storage. They may also include specialized hardware, such as graphics processing units (GPUs), to accelerate specific types of computations.
High-performance VMs are used in many applications such as finance, healthcare, scientific research, and media and entertainment, where high levels of computing power are required to process large amounts of data or perform complex computations. They are also used by cloud service providers to offer Infrastructure-as-a-Service (IaaS) solutions to customers who require high levels of computing power but do not want to invest in expensive on-premises hardware.
Overall, high-performance VMs offer organizations the flexibility and scalability of virtualization technology, while providing the computing power required to run demanding workloads.
CPU & GPU Colaborative Computing
Colab is a free Jupyter notebook environment from Google that runs in the cloud, providing access to computing resources including GPUs and TPUs. Colab notebooks allow you to write and execute Python code in your browser, visualize results, and share notebooks. Colab is ideal for machine learning education and research.
Jupyter Notebooks are open-source web applications that allow you to create and share documents with live code, equations, visualizations and text in various languages including Python and R. Notebooks are used extensively for teaching and research.
We have developed an interactive computing platform similar to Google Colaboratory (Colab). Our system provides access to computing resources including multi-GPU machines and scalable storage. Users can register for an account, charge their account balance, and access the service through our web portal.
Our goal is to make advanced computing more accessible for learning and research. Unlike Colab which provides Google’s TPUs, our system currently supports only GPU computing but offers unlimited runtime and flexible storage.
Our platform resembles Colab, providing Jupyter notebooks and scalable computing resources. However, we offer unlimited runtime and flexible storage, though currently only supporting GPUs, not TPUs. We aim to continue improving our platform to provide greater access to high-performance computing for learning and research.