A processor only understands instructions encoded in some numerical fashion, usually as binary numbers. Software tools, such as compilers , translate those high level languages into instructions that the processor can understand. Besides instructions, the ISA defines items in the computer that are available to a program—e. Instructions locate these available items with register indexes or names and memory addressing modes. The ISA of a computer is usually described in a small instruction manual, which describes how the instructions are encoded. Also, it may define short vaguely mnemonic names for the instructions.
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Scenarios include image rendering, fluid dynamics, financial risk modeling, oil exploration, drug design, and engineering stress analysis, among others. Here are some typical characteristics of big compute applications: The work can be split into discrete tasks, which can be run across many cores simultaneously.
Each task is finite. It takes some input, does some processing, and produces output. The entire application runs for a finite amount of time minutes to days. A common pattern is to provision a large number of cores in a burst, and then spin down to zero once the application completes. However, the system must handle node failures or application crashes.
For some applications, tasks are independent and can run in parallel. In other cases, tasks are tightly coupled, meaning they must interact or exchange intermediate results.
In that case, consider using high-speed networking technologies such as InfiniBand and remote direct memory access RDMA. When to use this architecture Computationally intensive operations such as simulation and number crunching. Simulations that are computationally intensive and must be split across CPUs in multiple computers s.
Simulations that require too much memory for one computer, and must be split across multiple computers. Long-running computations that would take too long to complete on a single computer. Smaller computations that must be run s or s of times, such as Monte Carlo simulations. Benefits High performance with " embarrassingly parallel " processing.
Can harness hundreds or thousands of computer cores to solve large problems faster. Access to specialized high-performance hardware, with dedicated high-speed InfiniBand networks. You can provision VMs as needed to do work, and then tear them down.
Challenges Managing the volume of number crunching Provisioning thousands of cores in a timely manner. For tightly coupled tasks, adding more cores can have diminishing returns. You may need to experiment to find the optimum number of cores. Using Azure Batch, you configure a VM pool, and upload the applications and data files.
Then the Batch service provisions the VMs, assign tasks to the VMs, runs the tasks, and monitors the progress. Batch can automatically scale out the VMs in response to the workload. Batch also provides job scheduling. With this approach, you must provision and manage the VMs and network infrastructure.
Consider this approach if you have existing HPC workloads and want to move some or all it to Azure. For more information, see Batch and HPC solutions for large-scale computing workloads.
The head node provides management and job scheduling services to the cluster. For tightly coupled tasks, use an RDMA network that provides very high bandwidth, low latency communication between VMs.
The cluster head node is on-premises. Related Articles.
Big compute architecture style
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