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1. |
Record Nr. |
UNICAMPANIAVAN0042156 |
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Autore |
Cohen-Tannoudji, Claude |
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Titolo |
1 / Claude Cohen-Tannoudji, Bernard Diu, Franck Laloe ; translated from the French by Susan Reid Hemley, Nicole Ostrowsky, Dan Ostrowsky |
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Pubbl/distr/stampa |
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New York, : Wiley-Interscience ; Paris, : Hermann, 2004 |
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ISBN |
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Descrizione fisica |
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Altri autori (Persone) |
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Diu, Bernard |
Laloe, Franck |
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Disciplina |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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2. |
Record Nr. |
UNINA9910830155003321 |
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Autore |
Carver Richard H. <1960-> |
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Titolo |
Modern multithreading [[electronic resource] ] : implementing, testing, and debugging multithreaded Java and C++/Pthreads/Win32 programs / / Richard H. Carver, Kuo-Chung Tai |
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Pubbl/distr/stampa |
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Hoboken, NJ, : Wiley, 2006 |
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ISBN |
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1-280-27765-3 |
9786610277650 |
0-470-24456-9 |
0-471-74417-4 |
0-471-74416-6 |
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Descrizione fisica |
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1 online resource (481 p.) |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Parallel programming (Computer science) |
Threads (Computer programs) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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MODERN MULTITHREADING; CONTENTS; Preface; 1 Introduction to Concurrent Programming; 1.1 Processes and Threads: An Operating System's View; 1.2 Advantages of Multithreading; 1.3 Threads in Java; 1.4 Threads in Win32; 1.5 Pthreads; 1.6 C++ Thread Class; 1.6.1 C++ Class Thread for Win32; 1.6.2 C++ Class Thread for Pthreads; 1.7 Thread Communication; 1.7.1 Nondeterministic Execution Behavior; 1.7.2 Atomic Actions; 1.8 Testing and Debugging Multithreaded Programs; 1.8.1 Problems and Issues; 1.8.2 Class TDThread for Testing and Debugging |
1.8.3 Tracing and Replaying Executions with Class Template sharedVariable1.9 Thread Synchronization; Further Reading; References; Exercises; 2 The Critical Section Problem; 2.1 Software Solutions to the Two-Thread Critical Section Problem; 2.1.1 Incorrect Solution 1; 2.1.2 Incorrect Solution 2; 2.1.3 Incorrect Solution 3; 2.1.4 Peterson's Algorithm; 2.1.5 Using the volatile Modifier; 2.2 Ticket-Based Solutions to the n-Thread Critical Section Problem; 2.2.1 Ticket |
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Algorithm; 2.2.2 Bakery Algorithm; 2.3 Hardware Solutions to the n-Thread Critical Section Problem; 2.3.1 Partial Solution |
2.3.2 Complete Solution2.3.3 Note on Busy-Waiting; 2.4 Deadlock, Livelock, and Starvation; 2.4.1 Deadlock; 2.4.2 Livelock; 2.4.3 Starvation; 2.5 Tracing and Replay for Shared Variables; 2.5.1 ReadWrite-Sequences; 2.5.2 Alternative Definition of ReadWrite-Sequences; 2.5.3 Tracing and Replaying ReadWrite-Sequences; 2.5.4 Class Template sharedVariable; 2.5.5 Putting It All Together; 2.5.6 Note on Shared Memory Consistency; Further Reading; References; Exercises; 3 Semaphores and Locks; 3.1 Counting Semaphores; 3.2 Using Semaphores; 3.2.1 Resource Allocation; 3.2.2 More Semaphore Patterns |
3.3 Binary Semaphores and Locks3.4 Implementing Semaphores; 3.4.1 Implementing P() and V(); 3.4.2 VP() Operation; 3.5 Semaphore-Based Solutions to Concurrent Programming Problems; 3.5.1 Event Ordering; 3.5.2 Bounded Buffer; 3.5.3 Dining Philosophers; 3.5.4 Readers and Writers; 3.5.5 Simulating Counting Semaphores; 3.6 Semaphores and Locks in Java; 3.6.1 Class countingSemaphore; 3.6.2 Class mutexLock; 3.6.3 Class Semaphore; 3.6.4 Class ReentrantLock; 3.6.5 Example: Java Bounded Buffer; 3.7 Semaphores and Locks in Win32; 3.7.1 CRITICAL_SECTION; 3.7.2 Mutex; 3.7.3 Semaphore; 3.7.4 Events |
3.7.5 Other Synchronization Functions3.7.6 Example: C++/Win32 Bounded Buffer; 3.8 Semaphores and Locks in Pthreads; 3.8.1 Mutex; 3.8.2 Semaphore; 3.9 Another Note on Shared Memory Consistency; 3.10 Tracing, Testing, and Replay for Semaphores and Locks; 3.10.1 Nondeterministic Testing with the Lockset Algorithm; 3.10.2 Simple SYN-Sequences for Semaphores and Locks; 3.10.3 Tracing and Replaying Simple PV-Sequences and LockUnlock-Sequences; 3.10.4 Deadlock Detection; 3.10.5 Reachability Testing for Semaphores and Locks; 3.10.6 Putting It All Together; Further Reading; References; Exercises |
4 Monitors |
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Sommario/riassunto |
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Master the essentials of concurrent programming,including testing and debuggingThis textbook examines languages and libraries for multithreaded programming. Readers learn how to create threads in Java and C++, and develop essential concurrent programming and problem-solving skills. Moreover, the textbook sets itself apart from other comparable works by helping readers to become proficient in key testing and debugging techniques. Among the topics covered, readers are introduced to the relevant aspects of Java, the POSIX Pthreads library, and the Windows Win32 Applications Programming In |
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3. |
Record Nr. |
UNINA9910595077403321 |
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Autore |
Wan Shibiao |
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Titolo |
Bioinformatics and Machine Learning for Cancer Biology |
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Pubbl/distr/stampa |
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Basel, : MDPI Books, 2022 |
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Descrizione fisica |
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1 electronic resource (196 p.) |
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Soggetti |
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Research & information: general |
Biology, life sciences |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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Cancer is a leading cause of death worldwide, claiming millions of lives each year. Cancer biology is an essential research field to understand how cancer develops, evolves, and responds to therapy. By taking advantage of a series of “omics” technologies (e.g., genomics, transcriptomics, and epigenomics), computational methods in bioinformatics and machine learning can help scientists and researchers to decipher the complexity of cancer heterogeneity, tumorigenesis, and anticancer drug discovery. Particularly, bioinformatics enables the systematic interrogation and analysis of cancer from various perspectives, including genetics, epigenetics, signaling networks, cellular behavior, clinical manifestation, and epidemiology. Moreover, thanks to the influx of next-generation sequencing (NGS) data in the postgenomic era and multiple landmark cancer-focused projects, such as The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), machine learning has a uniquely advantageous role in boosting data-driven cancer research and unraveling novel methods for the prognosis, prediction, and treatment of cancer. |
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