{"_buckets": {"deposit": "2778605d-5134-4e9a-9f7f-3cd36581659b"}, "_deposit": {"id": "7760", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "7760"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00007760"}, "item_10_biblio_info_6": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2006", "bibliographicIssueDateType": "Issued"}, "bibliographicPageEnd": "8", "bibliographicPageStart": "8", "bibliographic_titles": [{"bibliographic_title": "20th International Parallel and Distributed Processing Symposium"}]}]}, "item_10_description_4": {"attribute_name": "\u6284\u9332", "attribute_value_mlt": [{"subitem_description": "CDD weather derivatives are widely used to hedge weather risks and their fast and accurate pricing is an important problem in financial engineering. In this paper, we propose an efficient parallelization strategy of a pricing algorithm for the CDD derivatives. The algorithm uses the fast Gauss transform to compute the expected payoff of the derivative and has proved faster and more accurate than the conventional Monte Carlo method. However, speeding up the algorithm on a distributed-memory parallel computer is not straightforward because na\u0457ve parallelization will require a large amount of inter-processor communication. our new parallelization strategy exploits the structure of the fast Gauss transform and thereby reduces the amount of inter-processor communication considerably. Numerical experiments show that our strategy achieves up to 50 \uff05 performance improvement over the na\u0457ve one on an 16-node Mac G5 cluster and can compute the price of a representative CDD derivative in 7 seconds. This speed is adequate for almost any applications.", "subitem_description_type": "Abstract"}]}, "item_10_identifier_60": {"attribute_name": "URI", "attribute_value_mlt": [{"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/9478"}]}, "item_10_publisher_32": {"attribute_name": "\u51fa\u7248\u8005", "attribute_value_mlt": [{"subitem_publisher": "IEEE"}]}, "item_10_relation_11": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_type_id": {"subitem_relation_type_id_text": "http://dx.doi.org/10.1109/IPDPS.2006.1639615", "subitem_relation_type_select": "DOI"}}]}, "item_10_relation_8": {"attribute_name": "ISBN", "attribute_value_mlt": [{"subitem_relation_type_id": {"subitem_relation_type_id_text": "1-4244-0054-6", "subitem_relation_type_select": "ISBN"}}]}, "item_10_rights_12": {"attribute_name": "\u6a29\u5229", "attribute_value_mlt": [{"subitem_rights": "Copyright \u00a9 2006 IEEE. Reprinted from (relevant publication info). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Nagoya University\u2019s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org."}]}, "item_10_select_15": {"attribute_name": "\u8457\u8005\u7248\u30d5\u30e9\u30b0", "attribute_value_mlt": [{"subitem_select_item": "publisher"}]}, "item_10_text_14": {"attribute_name": "\u30d5\u30a9\u30fc\u30de\u30c3\u30c8", "attribute_value_mlt": [{"subitem_text_value": "application/pdf"}]}, "item_creator": {"attribute_name": "\u8457\u8005", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Yamamoto, Yusaku"}], "nameIdentifiers": [{"nameIdentifier": "22015", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "\u30d5\u30a1\u30a4\u30eb\u60c5\u5831", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2018-02-19"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "yamamoto_1.pdf", "filesize": [{"value": "1.1 MB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 1100000.0, "url": {"label": "yamamoto_1.pdf", "url": "https://nagoya.repo.nii.ac.jp/record/7760/files/yamamoto_1.pdf"}, "version_id": "b8bce3e1-a7f8-4a9b-9fe8-e780e7dda8e5"}]}, "item_language": {"attribute_name": "\u8a00\u8a9e", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "\u8cc7\u6e90\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "Efficient Parallel Implementation of a Weather Derivatives Pricing Algorithm based on the Fast Gauss Transform", "item_titles": {"attribute_name": "\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_title": "Efficient Parallel Implementation of a Weather Derivatives Pricing Algorithm based on the Fast Gauss Transform"}]}, "item_type_id": "10", "owner": "1", "path": ["320/321/322"], "permalink_uri": "http://hdl.handle.net/2237/9478", "pubdate": {"attribute_name": "\u516c\u958b\u65e5", "attribute_value": "2008-02-26"}, "publish_date": "2008-02-26", "publish_status": "0", "recid": "7760", "relation": {}, "relation_version_is_last": true, "title": ["Efficient Parallel Implementation of a Weather Derivatives Pricing Algorithm based on the Fast Gauss Transform"], "weko_shared_id": 3}