TY - GEN
T1 - Experimental study of compressed stack algorithms in limited memory environments
AU - Baffier, Jean François
AU - Diez, Yago
AU - Korman, Matias
N1 - Funding Information:
Partially supported by JST ERATO Grant Number JPMJER1305, Japan. 2 Partially supported by the Impact TRC project from Japan’s Science and Technology agency. 3 Partially supported by MEXT KAKENHI No. 12H00855, 15H02665, and 17K12635.
Funding Information:
1 Partially supported by JST ERATO Grant Number JPMJER1305, Japan. 2 Partially supported by the Impact TRC project from Japan’s Science and Technology agency. 3 Partially supported by MEXT KAKENHI No. 12H00855, 15H02665, and 17K12635.
Publisher Copyright:
© Jean-François Ba er, Yago Diez, and Matias Korman; licensed under Creative Commons License CC-BY
PY - 2018/6/1
Y1 - 2018/6/1
N2 - The compressed stack is a data structure designed by Barba et al. (Algorithmica 2015) that allows to reduce the amount of memory needed by a certain class of algorithms at the cost of increasing its runtime. In this paper we introduce the first implementation of this data structure and make its source code publicly available. Together with the implementation we analyse the performance of the compressed stack. In our synthetic experiments, considering di erent test scenarios and using data sizes ranging up to 230 elements, we compare it with the classic (uncompressed) stack, both in terms of runtime and memory used. Our experiments show that the compressed stack needs significantly less memory than the usual stack (this di erence is significant for inputs containing 2000 or more elements). Overall, with a proper choice of parameters, we can save a significant amount of space (from two to four orders of magnitude) with a small increase in the runtime (2.32 times slower on average than the classic stack). These results hold even in test scenarios specifically designed to be challenging for the compressed stack.
AB - The compressed stack is a data structure designed by Barba et al. (Algorithmica 2015) that allows to reduce the amount of memory needed by a certain class of algorithms at the cost of increasing its runtime. In this paper we introduce the first implementation of this data structure and make its source code publicly available. Together with the implementation we analyse the performance of the compressed stack. In our synthetic experiments, considering di erent test scenarios and using data sizes ranging up to 230 elements, we compare it with the classic (uncompressed) stack, both in terms of runtime and memory used. Our experiments show that the compressed stack needs significantly less memory than the usual stack (this di erence is significant for inputs containing 2000 or more elements). Overall, with a proper choice of parameters, we can save a significant amount of space (from two to four orders of magnitude) with a small increase in the runtime (2.32 times slower on average than the classic stack). These results hold even in test scenarios specifically designed to be challenging for the compressed stack.
KW - Convex hull
KW - Implementation
KW - Phrases Stack algorithms
KW - Time-space trade-o
UR - http://www.scopus.com/inward/record.url?scp=85063739344&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063739344&partnerID=8YFLogxK
U2 - 10.4230/LIPIcs.SEA.2018.19
DO - 10.4230/LIPIcs.SEA.2018.19
M3 - Conference contribution
AN - SCOPUS:85063739344
T3 - Leibniz International Proceedings in Informatics, LIPIcs
SP - 19:1-19:13
BT - 17th Symposium on Experimental Algorithms, SEA 2018
A2 - D'Angelo, Gianlorenzo
PB - Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
T2 - 17th Symposium on Experimental Algorithms, SEA 2018
Y2 - 27 June 2018 through 29 June 2018
ER -