This document provides an introduction to Java's Date and Time API. It discusses key classes like LocalDate, LocalDateTime, and ZonedDateTime that represent dates, times, and timestamps. It also covers formatting and parsing dates and times using DateTimeFormatter, and manipulating dates and times using factory and conversion methods. The document concludes that the API provides a powerful way to perform date and time calculations while modeling the ISO 8601 standard.
Porting and optimizing UniFrac for GPUsIgor Sfiligoi
Poster presented at PEARC20.
UniFrac is a commonly used metric in microbiome research for comparing microbiome profiles to one another (“beta diversity”). The recently implemented Striped UniFrac added the capability to split the problem into many independent subproblems and exhibits near linear scaling. In this poster we describe steps undertaken in porting and optimizing Striped Unifrac to GPUs. We reduced the run time of computing UniFrac on the published Earth Microbiome Project dataset from 13 hours on an Intel Xeon E5-2680 v4 CPU to 12 minutes on an NVIDIA Tesla V100 GPU, and to about one hour on a laptop with NVIDIA GTX 1050 (with minor loss in precision). Computing UniFrac on a larger dataset containing 113k samples reduced the run time from over one month on the CPU to less than 2 hours on the V100 and 9 hours on an NVIDIA RTX 2080TI GPU (with minor loss in precision). This was achieved by using OpenACC for generating the GPU offload code and by improving the memory access patterns. A BSD-licensed implementation is available, which produces a Cshared library linkable by any programming language.
Porting and optimizing UniFrac for GPUsIgor Sfiligoi
Poster presented at PEARC20.
UniFrac is a commonly used metric in microbiome research for comparing microbiome profiles to one another (“beta diversity”). The recently implemented Striped UniFrac added the capability to split the problem into many independent subproblems and exhibits near linear scaling. In this poster we describe steps undertaken in porting and optimizing Striped Unifrac to GPUs. We reduced the run time of computing UniFrac on the published Earth Microbiome Project dataset from 13 hours on an Intel Xeon E5-2680 v4 CPU to 12 minutes on an NVIDIA Tesla V100 GPU, and to about one hour on a laptop with NVIDIA GTX 1050 (with minor loss in precision). Computing UniFrac on a larger dataset containing 113k samples reduced the run time from over one month on the CPU to less than 2 hours on the V100 and 9 hours on an NVIDIA RTX 2080TI GPU (with minor loss in precision). This was achieved by using OpenACC for generating the GPU offload code and by improving the memory access patterns. A BSD-licensed implementation is available, which produces a Cshared library linkable by any programming language.
A minimal introduction to Python non-uniform fast Fourier transform (pynufft)Jyh-Miin Lin
Welcome to pynufft's Documentation!
Python non-uniform fast Fourier transform was designed and developed for image reconstruction in Python. Pynufft was written in pure Python and is based on numerical libraries, such as Numpy, Scipy (matplotlib for displaying examples). CUDA computing is experimentally supported.
Pynufft can be installed from Pypi (pip install pynufft). The source can be obtained from https://github.com/jyhmiinlin/pynufft.
Basic concept of Deep Learning with explaining its structure and backpropagation method and understanding autograd in PyTorch. (+ Data parallism in PyTorch)
First and second order semi-Markov chains for wind speed modelingNozir Shokirov
An overview of Guglelmo D'Amic et al. paper titled as "First and second order semi-Markov chains for wind speed modeling" from the journal of Physica A 2012.
Localized Electrons with Wien2k
LDA+U, EECE, MLWF, DMFT
Elias Assmann
Vienna University of Technology, Institute for Solid State Physics
WIEN2013@PSU, Aug 14
Blind separation of complex-valued satellite-AIS data for marine surveillance...IJECEIAES
In this paper, the problem of the blind separation of complex-valued Satellite-AIS data for marine surveillance is addressed. Due to the specific properties of the sources under consideration: they are cyclo-stationary signals with two close cyclic frequencies, we opt for spatial quadratic time-frequency domain methods. The use of an additional diversity, the time delay, is aimed at making it possible to undo the mixing of signals at the multi-sensor receiver. The suggested method involves three main stages. First, the spatial generalized mean Ambiguity function of the observations across the array is constructed. Second, in the Ambiguity plane, Delay-Doppler regions of high magnitude are determined and Delay-Doppler points of peaky values are selected. Third, the mixing matrix is estimated from these Delay-Doppler regions using our proposed non-unitary joint zero-(block) diagonalization algorithms as to perform separation.
This presentation provides an overview of using the Java SE 8 Date & Time API. It covers how to:
1. Create and manage date-based and time-based events including a combination of date and time into a single object using LocalDate, LocalTime, LocalDateTime, Instant, Period, and Duration
2. Work with dates and times across timezones and manage changes resulting from daylight savings including format date and times values
3. Define and create and manage date-based and time-based events using Instant, Period, Duration, and TemporalUnit
A minimal introduction to Python non-uniform fast Fourier transform (pynufft)Jyh-Miin Lin
Welcome to pynufft's Documentation!
Python non-uniform fast Fourier transform was designed and developed for image reconstruction in Python. Pynufft was written in pure Python and is based on numerical libraries, such as Numpy, Scipy (matplotlib for displaying examples). CUDA computing is experimentally supported.
Pynufft can be installed from Pypi (pip install pynufft). The source can be obtained from https://github.com/jyhmiinlin/pynufft.
Basic concept of Deep Learning with explaining its structure and backpropagation method and understanding autograd in PyTorch. (+ Data parallism in PyTorch)
First and second order semi-Markov chains for wind speed modelingNozir Shokirov
An overview of Guglelmo D'Amic et al. paper titled as "First and second order semi-Markov chains for wind speed modeling" from the journal of Physica A 2012.
Localized Electrons with Wien2k
LDA+U, EECE, MLWF, DMFT
Elias Assmann
Vienna University of Technology, Institute for Solid State Physics
WIEN2013@PSU, Aug 14
Blind separation of complex-valued satellite-AIS data for marine surveillance...IJECEIAES
In this paper, the problem of the blind separation of complex-valued Satellite-AIS data for marine surveillance is addressed. Due to the specific properties of the sources under consideration: they are cyclo-stationary signals with two close cyclic frequencies, we opt for spatial quadratic time-frequency domain methods. The use of an additional diversity, the time delay, is aimed at making it possible to undo the mixing of signals at the multi-sensor receiver. The suggested method involves three main stages. First, the spatial generalized mean Ambiguity function of the observations across the array is constructed. Second, in the Ambiguity plane, Delay-Doppler regions of high magnitude are determined and Delay-Doppler points of peaky values are selected. Third, the mixing matrix is estimated from these Delay-Doppler regions using our proposed non-unitary joint zero-(block) diagonalization algorithms as to perform separation.
This presentation provides an overview of using the Java SE 8 Date & Time API. It covers how to:
1. Create and manage date-based and time-based events including a combination of date and time into a single object using LocalDate, LocalTime, LocalDateTime, Instant, Period, and Duration
2. Work with dates and times across timezones and manage changes resulting from daylight savings including format date and times values
3. Define and create and manage date-based and time-based events using Instant, Period, Duration, and TemporalUnit
These slides explores php date and time library. You will find, what is UNIX time stamp, how to use php's date functions. A beginner introduction by programmer blog
Advanced patterns in asynchronous programmingMichael Arenzon
Using Futures as a basic building block for concurrent, async code has become pervasive in the past few years and for a good reason. However, when moving from the traditional synchronous code to the async one, a set of patterns that were obvious to implement before now seem to be more challenging. The aim of this talk is to show few examples of these patterns implemented with Scala futures in an async and non blocking manner. We will present the usage pattern and the implementation in order to show the principles of properly handling async code.
Libraries and History
The “old” Date/Calendar classes
The new (≥Java8) java.time package
Basic concepts
Main classes
Date operations
Dealing with SQL dates
Teaching material for the course of "Tecniche di Programmazione" at Politecnico di Torino in year 2014/2015. More information: http://bit.ly/tecn-progr
Surviving as a zombie is tough... with the constant risks of sunlight, fire, and pesky mobs, doing your job of infecting the local villagers can be deadly. Fortunately, with the new JavaFX ZombieTime app, powered by the JSR 310 Date and Time API, you can rest easy. With built-in time zone and DST support you no longer have to worry about roaming around under the scorching hot sun. Accurately calculate out how long you have to infect the villagers before you decompose using Durations. And coordinate global attacks on the humans by syncing with your undead brethren on Instants. With the power of Java 8, eradicating the human race with a highly infectious virus has never been easier!
This presentation is designed to teach Java Date and Time APIs to the undead, but the living are welcome to be our "guests". You may also learn some JavaFX in the process -- that is entirely my fault. Any correlation between the characters and events in this presentation and the impending extinction of mankind is purely coincidental.
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
Accelerate Enterprise Software Engineering with PlatformlessWSO2
Key takeaways:
Challenges of building platforms and the benefits of platformless.
Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience.
How Choreo enables the platformless experience.
How key concepts like application architecture, domain-driven design, zero trust, and cell-based architecture are inherently a part of Choreo.
Demo of an end-to-end app built and deployed on Choreo.
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...Globus
The U.S. Geological Survey (USGS) has made substantial investments in meeting evolving scientific, technical, and policy driven demands on storing, managing, and delivering data. As these demands continue to grow in complexity and scale, the USGS must continue to explore innovative solutions to improve its management, curation, sharing, delivering, and preservation approaches for large-scale research data. Supporting these needs, the USGS has partnered with the University of Chicago-Globus to research and develop advanced repository components and workflows leveraging its current investment in Globus. The primary outcome of this partnership includes the development of a prototype enterprise repository, driven by USGS Data Release requirements, through exploration and implementation of the entire suite of the Globus platform offerings, including Globus Flow, Globus Auth, Globus Transfer, and Globus Search. This presentation will provide insights into this research partnership, introduce the unique requirements and challenges being addressed and provide relevant project progress.
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
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Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
9. What's ISO 8601?
• International Standard
• Using Gregorian calendar
• Base of JIS X 0301, RFC 3339, etc.
• Incompatible with Unix time
(java.util.Date/Calendar are based on Unix time)
10. Time (w/o Time Zone)
• hh:mm:ss e.g. 14:30:15
• hh:mm e.g. 14:30
• hh e.g. 14
• hh:mm:ss.s e.g. 14:30:15.250
11. Time (w/Time Zone)
• Add suffix - offset from UTC (±hh:mm)
• hh:mm:ss±hh:mm
• e.g. 14:30:45+09:00 (Asia/Tokyo)
• e.g. 21:30:45-08:00 (America/Los_Angeles)
• e.g. 05:30:45Z (UTC)
12. Date
• calendar date:
YYYY-MM-DD e.g. 2015-07-11
• ordinal date:
YYYY-DDD e.g. 2015-192
• week date:
YYYY-Www-D e.g. 2015-W29-6
13. Date (Short)
• year-month:
YYYY-MM e.g. 2015-07
• year:
YYYY e.g. 2015
• month-day:
--MM-DD e.g. --07-11
14. Date and Time
• Concat date and time using 'T'
• If it needs, add offset (Time Zone)
• YYYY-MM-DDThh:mm:ss
e.g. 2015-07-11T14:45:30
• YYYY-MM-DDThh:mm:ss±hh:mm
e.g. 2015-07-10T21:45:30-08:00
15. Duration
• Time amount between time points
• date : nYnMnD e.g. 1Y3M22D
• time : nHnMnS e.g. 9H30M45S
• date and time : nYnMnDTnHnMnS
e.g. 1Y3M22DT9H30M45S
16. Period
• Range between dates/times
• YYYY-MM-DD/YYYY-MM-DD (start/end)
• YYYY-MM-DD/PnYnMnD (start/duration)
• PnYnMnD/YYYY-MM-DD (duration/end)
• PnYnMnD (duration)
17. Definition of week
• a week = 7 days
• 1st week contains
the first Thursday of
the year.
• a year contents 52 or
53 weeks.
1 Monday
2 Tuesday
3 Wednesday
4 Thursday
5 Friday
6 Saturday
7 Sunday
19. Packages
Package Description Use
java.time Basic classes usual
java.time.format Date and Time Formatter partial
java.time.chrono Chronology supports partial
java.time.temporal Low-level API rare
java.time.zone Low-level API rare
20. Date and Time classes
Class Field T/Z ISO 8601
LocalDate Date N/A YYYY-MM-DD
LocalDateTime Date/Time N/A YYYY-MM-DDThh:mm:ss
LocalTime Time N/A hh:mm:ss
OffsetDateTime Date/Time offset YYYY-MM-DDThh:mm:ss±hh:mm
OffsetTime Time offset hh:mm:ss±hh:mm
ZonedDateTime Date/Time zone id N/A
21. Factory methods
Oper. Description
of-
Create from fields.
e.g. LocalDate.of(2015, 7, 11)
now
Create from a clock.
e.g. LocalDate.now()
from
Create from other Temporal objects.
e.g. LocalDate.from(LocalDateTime.now())
parse
Create from String. (may use a formatter)
e.g. LocalDate.parse("2015-07-11")
25. Obtain/Modify methods
Oper. Description
get-
Obtain the value of a field.
e.g. LocalDate.now().getYear()
e.g. LocalDate.now().get(YEAR)
with-
Modify the value of a field (returns a copy).
e.g. LocalDate.now().withYear(2016)
e.g. LocalDate.now().with(2016, YEAR)
27. Arithmetric methods
Oper. Description
plus-
Add a field value. (returns a copy)
e.g. LocalDate.now().plusDays(7)
e.g. LocalDate.now().plus(7, DAYS)
minus-
subtract a field value. (returns a copy)
e.g. LocalDate.now().minusDays(7)
e.g. LocalDate.now().minus(7, DAYS)
29. Compare methods
Oper. Description
isBefore
e.g. today.isBefore(yesterday) ; false
e.g. today.isBefore(today) ; false
e.g. today.isBefore(tomorrow) ; true
isEqual
e.g. today.isEqual(yesterday) ; false
e.g. today.isEqual(today) ; true
e.g. today.isEqual(tomorrow) ; false
isAfter
e.g. today.isAfter(yesterday) ; true
e.g. today.isAfter(today) ; false
e.g. today.isAfter(tomorrow) ; false
30. Format/Parse methods
Method Description
toString()
Format using default formatter
(Instance method)
format(DateTimeFormatter f)
Format using custom formatter
(Instance method)
parse(String s)
Parse using default formatter
(Factory method)
parse(String s,
DateTimeFormatter f)
Parse using custom formatter
(Factory method)
31. DateTimeFormatter
1. Created by ofPattern factory (usually)
e.g. ofPettern("uuuu/MM/dd")
2. Select from pre-defined patterns:
• ISO_LOCAL_DATE
• ISO_OFFSET_TIME
• ISO_ZONED_DATE_TIME
3. Created by DateTimeFomatterBuilder
32. ResolverStyle
• One of formatter option (enum)
• LENIENT, SMART (default), STRICT
• Modifing method: withResolverStyle
• If it is STRICT mode, Pattern 'y' must
be used with 'G'
e.g. NO: yyyy/MM/dd OK: Gyyyy/MM/dd
35. Duration and Period
• Representation of temporal amount
correspond with "period" (ISO 8601).
• Period is the date part of "period",
i.e. formatted as "P1Y2M3D"
• Duration is the time part of "period",
i.e. formatted as "PT15H30M45D"
37. Instant
• Representation of a time-point
• The precision is a nano second
• The epoch is 1970-01-01T00:00:00Z
• The only interface to java.util.Date
38. Clock
• Provider of the current instant.
• Zone relative, fixed and custom.
• By default, it uses the clock relative
current zone.
• now method (LocalDate, et al.) creates
a temporal instance from a clock.
39. Fixed Clock
• Clock always provides same instant.
• It's very useful for application testing.
Clock clock = Clock.fixed(instant,
ZoneId.systemDefault);
!
// Using fixed clock
LocalDateTime.now(clock);
41. ZoneId is ...
• ID/tag of a time zone.
• The representation of time zone is
ZoneRules.
• There is the system default value.
• Abstract class; derived to ZoneOffset
and ZoneRegion (implicit).
42. ZoneOffset is ...
• ID/tag of a time zone for fixed offsets.
• Contains the offset from UTC.
• Used for OffsetDateTime/OffsetTime.
• Used for ZonedDateTime (as ZoneId).
• Defines transitions of ZoneRules.
44. ZoneId.of(String zoneId)
1. Fixed offsets
e.g. "+09:00", "Z"
-> instance of ZoneOffset
2. Geographical regions
e.g. "Asia/Tokyo"
-> instance of ZoneRegion
46. OffsetDateTime vs.
ZonedDateTime
• OffsetDateTime is based on ISO 8601
but ZonedDateTime is not.
• ZonedDateTime is adapt to daylight
savings easily. OffsetDateTime is not.
• Zones may be changed because of
region or country convenience. But
offsets are never.
48. What's Date and Time API?
• Modeling of ISO 8601
• Many classes, but ease of use
• Powerful Date/Time calculations
(See also TemporalAdjuster)
• Many extention points
(See also java.time.chrono.*)
49. How to study?
• Learn ISO 8601 (JIS X 0301)
• Master to use LocalDate
• Know why exists Local/Offset/Zoned
• Set priority to the classes
• Trial and error!
50. Introduction to Date and Time API III
HASUNUMA Kenji
k.hasunuma@coppermine.jp
Twitter: @khasunuma