Yazar
Yılmaz, Buse, Aktemur, Tankut Barış, Garzaran, M. J., Kamin, S., Kıraç, Mustafa Furkan
Basım Tarihi
2016-04
Basım Yeri
-
ACM
Konu
Performance, Experimentation, Measurement, Autotuning, Runtime code generation, Sparse matrix-vector multiplication
Tür
Süreli Yayın
Dil
İngilizce
Dijital
Evet
Yazma
Hayır
Kütüphane
Özyeğin Üniversitesi
Demirbaş Numarası
1544-3973
Kayıt Numarası
35155ed4-cde3-432f-be0b-588d015a69fe
Lokasyon
Computer Science
Tarih
2016-04
Notlar
Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin
Runtime specialization is used for optimizing programs based on partial information available only at runtime. In this paper we apply autotuning on runtime specialization of Sparse Matrix-Vector Multiplication to predict a best specialization method among several. In 91% to 96% of the predictions, either the best or the second-best method is chosen. Predictions achieve average speedups that are very close to the speedups achievable when only the best methods are used. By using an efficient code generator and a carefully designed set of matrix features, we show the runtime costs can be amortized to bring performance benefits for many real-world cases.
DOI
10.1145/2851500
Cilt
13
