Abstract
In previous chapters, we discussed several in-/near-memory computing accelerators for several applications in machine learning, databases, graph processing, and genomics. In this chapter we discuss the programming interfaces exposed by these accelerators and the trade-offs involved. We begin by discussing some of the programming models proposed for the early near-memory processing architectures in Section 7.1. Later, we discuss the design trade-offs made in recent domain-specific programming models in Section 7.2. We end by explaining the data-parallel programming models and compilation strategies adopted by recent in-memory computing architectures in Section 7.3.
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Fujiki, D., Wang, X., Subramaniyan, A., Das, R. (2021). Programming Models. In: In-/Near-Memory Computing. Synthesis Lectures on Computer Architecture. Springer, Cham. https://doi.org/10.1007/978-3-031-01772-8_7
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DOI: https://doi.org/10.1007/978-3-031-01772-8_7
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-00644-9
Online ISBN: 978-3-031-01772-8
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