Search Results for "parallel computing"
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Showing 5 open source projects for "parallel computing"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Keep company data safe with Chrome EnterpriseMake AI work your way with Chrome Enterprise. Block unapproved sites and set custom data controls that align with your company's policies.
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Chapel
a Productive Parallel Programming Language
Chapel is an emerging parallel programming language whose design and development are being led by HPE in collaboration with academia, computing labs, and industry. Chapel's goal is to improve the productivity of parallel programmers, from laptops to supercomputers. **Please note that Chapel development has moved to GitHub** -
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Tsunami is an open-source high performance computing language. With it you can write streaming data-parallel algorithms that utilize GPGPUs for orders-of-magnitude speed-up with the ease of writing sequential algorithms.
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DKU is an embedded parallel language, or "piggy-back" language that uses function calls to invoke the language's execution model. It tunes task-size to the hardware while hiding hardware details for high performance portability of parallel code.
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LIME (Less-is-More) is parallel/concurrent programming environment based on C. Internally, it uses XML technology to describe tasks and their dependencies. Externally, it offers the ANSI C99 programming as well as a set of visually-oriented interfaces.
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Cloud-based help desk software with ServoDeskWhat if You Could Automate 90% of Your Repetitive Tasks in Under 30 Days? At ServoDesk, we help businesses like yours automate operations with AI, allowing you to cut service times in half and increase productivity by 25% - without hiring more staff.
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The CodeTime platform covers every aspect of parallel software from authoring, through distribution, to run-time. Its goals are: high programmer productivity; write once, run high performance anywhere; and wide acceptance.
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