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| content="Apache Ignite enables high-performance computing by providing APIs for data and |
| compute-intensive calculations. Turn your commodity hardware or cloud environment into a distributed supercomputer."/> |
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| <title>High-Performance Computing</title> |
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| <h1><strong>High-Performance Computing</strong> With Apache Ignite</h1> |
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| </header> |
| <div class="container"> |
| <p> |
| High-performance computing (HPC) is the ability to process data and perform complex calculations at high |
| speeds. |
| Using Apache Ignite® as a high-performance compute cluster, you can turn a group of commodity machines or a |
| cloud environment into a distributed supercomputer of interconnected Ignite nodes. Ignite enables speed and scale |
| by processing records in memory and reducing network utilization with APIs for data and compute-intensive |
| calculations. Those APIs implement the MapReduce paradigm and allow you to run arbitrary tasks across the |
| cluster of nodes. |
| </p> |
| <img class="diagram-right img-fluid" src="/images/svg-diagrams/high_performance_compute.svg" alt="High-Performance Computing With Apache Ignite" /> |
| |
| |
| <h2>Co-located Processing</h2> |
| <p> |
| Ignite uses the notion of co-located processing to guide HPC workloads implementations in distributed |
| in-memory environments. Co-located processing increases the performance of your complex calculations by |
| running them straight on the Ignite cluster nodes. These calculations are done only on local data sets |
| available on the nodes, thus avoiding data shuffling over the network and resulting in orders of magnitude |
| increase in performance. |
| </p> |
| |
| <p> |
| To exploit the co-located processing in practice, first, you need to co-locate data by storing related |
| records on the same cluster node. As an example of related or co-located data, consider your bank account |
| and transactions posted to it. Once you set <code>accountID</code> as an affinity key for the |
| <code>Transactions</code> table, you'll instruct Ignite to store all transactions for your |
| <code>accountId</code> on the same node that keeps the record of your account in the |
| <code>Accounts</code> table. Now let's say a payment processing system sends a compute task that |
| verifies previous transactions of your account. Since the data is co-located, Ignite will execute this |
| task directly on the node that stores your account record with all completed transactions and finish the |
| verification locally on that machine instead of pulling all the transactions back to the application |
| over the network. This method of executing a task on the node where the data resides provides |
| exceptionally high performance.The effect is even more significant when the system needs to process |
| millions of transactions per second, verifying billions of previously completed payments. |
| </p> |
| |
| <h2>Compute APIs</h2> |
| |
| <p> |
| Ignite provides compute APIs (also known as compute grid) for creating and scheduling custom |
| tasks of arbitrary complexity. The APIs implement MapReduce paradigm and are presently available for Java, |
| C#, and C++. |
| </p> |
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| <a href="/features/collocated-processing.html"> |
| Co-located processing <i class="fas fa-angle-double-right"></i> |
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| <a href="https://apacheignite.readme.io/docs/compute-grid" target="docs"> |
| Compute APIs <i class="fas fa-angle-double-right"></i> |
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| Multi-Tier Storage <i class="fas fa-angle-double-right"></i> |
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