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+        </style><title>FProPer 2024: Proceedings of the 1st ACM SIGPLAN International Workshop on Functional Programming for Productivity and Performance</title></head><body><div id="DLtoc"><div id="DLheader"><h1>FProPer 2024: Proceedings of the 1st ACM SIGPLAN International Workshop on Functional Programming for Productivity and Performance</h1><a class="DLcitLink" title="Go to the ACM Digital Library for additional information about this proceeding" referrerpolicy="no-referrer-when-downgrade" href="https://dl.acm.org/doi/proceedings/10.1145/3677997"><img class="DLlogo" alt="Digital Library logo" height="30" src="https://dl.acm.org/specs/products/acm/releasedAssets/images/footer-logo1.png">
+                Full Citation in the ACM Digital Library
+            </a></div><div id="DLcontent"><h2>SESSION: Papers</h2>
+							<h3><a class="DLtitleLink" title="Full Citation in the ACM Digital Library" referrerpolicy="no-referrer-when-downgrade" href="https://dl.acm.org/doi/10.1145/3677997.3678226">A Comparison of OpenCL, CUDA, and HIP as Compilation Targets for a Functional Array Language</a></h3><ul class="DLauthors"><li class="nameList Last">Troels Henriksen</li></ul><div class="DLabstract"><div style="display:inline">
+				<p>This paper compares OpenCL, CUDA, and HIP as compilation targets for
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+Futhark, a functional array language. We compare the performance of
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+OpenCL versus CUDA, and OpenCL versus HIP, on the code generated by
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+the Futhark compiler on a collection of 48 application benchmarks on
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+two different GPUs. Despite the generated code in most cases being
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+equivalent, we observe significant performance differences on the
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+same hardware, ranging from 0.42x to 1.72x in the most extreme
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+cases. We identify the root causes of most of these differences,
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+many of which are due to relatively superficial details such as
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+inconsistent defaults regarding compiler optimisation and numerical
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+accuracy, although a few remain mysterious.</p>
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+							<h3><a class="DLtitleLink" title="Full Citation in the ACM Digital Library" referrerpolicy="no-referrer-when-downgrade" href="https://dl.acm.org/doi/10.1145/3677997.3678227">Fusing Gathers with Integer Linear Programming</a></h3><ul class="DLauthors"><li class="nameList">David van Balen</li><li class="nameList">Gabriele Keller</li><li class="nameList">Ivo Gabe de Wolff</li><li class="nameList Last">Trevor L. McDonell</li></ul><div class="DLabstract"><div style="display:inline">
+				<p>We present an Integer Linear Programming based approach to finding the optimal fusion strategy for combinator-based parallel programs. While combinator-based languages or libraries provide a convenient interface for programming parallel hardware, fusing combinators to more complex operations is essential to achieve the desired performance. Our approach is not only suitable for languages with the usual map, fold, scan, indexing and scatter operations, but also gather operations, which access arrays in arbitrary order, and therefore goes beyond the traditional producer-consumer fusion. It can be parametrised with appropriate cost functions, and is fast enough to be suitable for just-in-time compilation.</p>
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