{"id":671754,"date":"2020-07-06T00:04:11","date_gmt":"2020-07-06T07:04:11","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=671754"},"modified":"2020-07-06T00:05:45","modified_gmt":"2020-07-06T07:05:45","slug":"effect-handlers-in-haskell-evidently","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/effect-handlers-in-haskell-evidently\/","title":{"rendered":"Effect Handlers in Haskell, Evidently"},"content":{"rendered":"

Algebraic effect handlers offer an alternative to monads to incorporate
\neffects in Haskell. In recent work Xie _et al._ show how to give
\nsemantics to effect handlers in terms of plain polymorphic lambda
\ncalculus through _evidence translation_. Besides giving precise
\nsemantics, this translation also allows for potentially more efficient
\nimplementations. Here we present the first implementation of this
\ntechnique as a library for effect handlers in Haskell. We show how the
\ndesign naturally leads to a concise effect interface and how evidence
\ntranslation enables evaluating _tail resumptive_ operations _in-place_.
\nWe give detailed benchmark results where our library performs well with
\nrespect to other approaches.<\/p>\n","protected":false},"excerpt":{"rendered":"

Algebraic effect handlers offer an alternative to monads to incorporate effects in Haskell. In recent work Xie _et al._ show how to give semantics to effect handlers in terms of plain polymorphic lambda calculus through _evidence translation_. Besides giving precise semantics, this translation also allows for potentially more efficient implementations. Here we present the first 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SIGPLAN","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2020\/07\/effev.pdf","id":"671760","title":"effev","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[{"id":671760,"url":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2020\/07\/effev.pdf"}],"msr-author-ordering":[{"type":"text","value":"Ningning Xie","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Daan Leijen","user_id":31497,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Daan 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