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Update README, licensing, build scripts #44
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aregm
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Jun 30, 2022
README.md
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| # Heterogeneous Data Kernels | |||
| # oneAPI Heterogeneous Data Kernels | |||
| oneHDK is a low-level execution library for analytic data processing. | |||
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data analytics processing? Swap the adjective.
README.md
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| Cloning a project with submodules. Either use `git clone --recurse-submodules` to clone the repo, or clone as normal and then run: | ||
| ### Storage | ||
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| `ArrowStorage` is the default (and only available) HDK storage layer. `ArrowStorage` provides storage support for [Apache Arrow](https://github.com/apache/arrow) format data. The storage layer must be explicitly initialized: |
README.md
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| HDK is used as a fast execution backend in [Modin](https://github.com/intel-ai/modin). The HDK library provides a set of components for federating analytic queries to an execution backend based on [OmniSciDB](https://github.com/intel-ai/omniscidb). Currently, HDK targets OLAP-style queries expressed as relational algebra or SQL. Major and immediate project priorities include: | ||
| - Introducing a HDK-specific IR and set of optimizations to reduce reliance on RelAlg and improve extensibility of the query API. | ||
| - Supporting heterogeneous device execution, where a query is split across a set of hardware devices (e.g. CPU and GPU) for best performance. We have developed an initial cost model for heterogeneous execution. | ||
| - Improving performance of the CPU backend on Modin-specific queries and current-generation data science workstations and servers. |
| A low-level execution library for analytic data processing. | ||
| HDK is used as a fast execution backend in [Modin](https://github.com/intel-ai/modin). The HDK library provides a set of components for federating analytic queries to an execution backend based on [OmniSciDB](https://github.com/intel-ai/omniscidb). Currently, HDK targets OLAP-style queries expressed as relational algebra or SQL. Major and immediate project priorities include: | ||
| - Introducing a HDK-specific IR and set of optimizations to reduce reliance on RelAlg and improve extensibility of the query API. | ||
| - Supporting heterogeneous device execution, where a query is split across a set of hardware devices (e.g. CPU and GPU) for best performance. We have developed an initial cost model for heterogeneous execution. |
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Supporting both Intel and Nvidia GPU
README.md
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| A low-level execution library for analytic data processing. | ||
| HDK is used as a fast execution backend in [Modin](https://github.com/intel-ai/modin). The HDK library provides a set of components for federating analytic queries to an execution backend based on [OmniSciDB](https://github.com/intel-ai/omniscidb). Currently, HDK targets OLAP-style queries expressed as relational algebra or SQL. Major and immediate project priorities include: |
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Also can be used standalone through pyHDK
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Comments addressed w/ latest push. |
aregm
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Jul 1, 2022
vlad-penkin
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Jul 1, 2022
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