THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Monthly Notices is one of the three largest general primary astronomical research publications. It is an international journal, published by the Royal Astronomical Society. This article 1 describes its publication policy and practice.

Monthly Notices of the Royal Astronomical Society

This book makes a clear presentation of the traditional topics included in a course of undergraduate parallel programming. As explained by the authors, it was developed from their own experience in classrooms, introducing their students to parallel programming. It can be used almost directly to teach basic parallel programming.

/pdf/parallel-programming-techniques-and-applications-using-1g2pyrr6g4.pdf

Parallel programming: techniques and applications using networked workstations and parallel computers. Barry Wilkinson, C. Michael Allen

Acute myelogenous leukemia (AML) is a subtype of acute leukemia, which is prevalent among adults. The average age of a person with AML is 65 years. The need for automation of leukemia detection arises since current methods involve manual examination of the blood smear as the first step toward diagnosis. This is time-consuming, and its accuracy depends on the operator's ability. In this paper, a simple technique that automatically detects and segments AML in blood smears is presented. The proposed method differs from others in: 1) the simplicity of the developed approach; 2) classification of complete blood smear images as opposed to subimages; and 3) use of these algorithms to segment and detect nucleated cells. Computer simulation involved the following tests: comparing the impact of Hausdorff dimension on the system before and after the influence of local binary pattern, comparing the performance of the proposed algorithms on subimages and whole images, and comparing the results of some of the existing systems with the proposed system. Eighty microscopic blood images were tested, and the proposed framework managed to obtain 98% accuracy for the localization of the lymphoblast cells and to separate it from the subimages and complete images.

Automated Screening System for Acute Myelogenous Leukemia Detection in Blood Microscopic Images

Harnessing the massively parallel architectures soon to become available into efficient algorithmic cooperation is one of the most important intellectual challenges facing Computer Science today. To the theoretician, the task seems similar to thiat of understanding the issues involved in the performance of sequential algorithms (which motivated Knuth’s books, among other important works), only mfinitely more complex. In sequential computation, th.e design process involves (a) choosing an algorithm and (b) analyzing it (mostly, counting its steps). In the parallel context, however, we have at least four stages: (1) Choose the algorithm (say, a directed acyclic graph (dag) indicating the elementary computations and their interdependence, a model in which evaluation of sequential performance is trivial). (2) Choose a particular multiprocessor architecture. (3) Find a schedule whereby the algorithm is executed on the processors (so that all necessary data a:re available at the appropriate processor at the time of each computation). (4) Only now can we talk about the performance of the algorithm, measured as the makespan of the schedule (elapsed time for computing the last result). In our opinion, it is this multi-layered nature of the problem that lies at the heart of the difficulties encountered in the development of the necessary ideas, principles, and tools for the design of parallel algorithms. Is there a way to shortcut the process, thus improving our chances of finally gaining some insight into parallel algorithms? It would be very interesting if we could combine stages (3) and (4) into a single step whereby the performance of the algorithm cho-

Towards an Architecture-Independent Analysis of Parallel Algorithms (Extended Abstract)

HPC users aim to improve their execution times without particular regard for increasing system utilization. On the contrary, HPC operators favor increasing the number of executed applications per time unit and increasing system utilization. This difference in the preferences promotes the following operational model. Applications execute on exclusively-allocated computing resources for a specific time and applications are assumed to utilize the allocated resources efficiently. In many cases, this operational model is inefficient, i.e., applications may not fully utilize their allocated resources. This inefficiency results in increasing application execution time and decreasing system utilization. In this work, we propose a resourceful coordination approach (RCA) that enables the cooperation between, currently independent, batch- and application-level schedulers. RCA enables application schedulers to share their allocated but idle computing resources with other applications through the batch system. The effective system performance (ESP) benchmark is used to assess the proposed approach. The results show that RCA increased system utilization up to 12.6% and decreased system makespan by the same percent without affecting applications' performance.

A Resourceful Coordination Approach for Multilevel Scheduling.

Loop scheduling techniques aim to achieve load-balanced executions of scientific applications. Dynamic loop self-scheduling (DLS) libraries for distributed-memory systems are typically MPI-based and employ a centralized chunk calculation approach (CCA) to assign variably-sized chunks of loop iterations. We present a distributed chunk calculation approach (DCA) that supports various types of DLS techniques. Using both CCA and DCA, twelve DLS techniques are implemented and evaluated in different CPU slowdown scenarios. The results show that the DLS techniques implemented using DCA outperform their corresponding ones implemented with CCA, especially in extreme system slowdown scenarios.

A Distributed Chunk Calculation Approach for Self-scheduling of Parallel Applications on Distributed-memory Systems

The size of computing systems and the number of their components steadily increase. The volume of generated system logs is in proportion to this increase. Storing system logs for analyzing and diagnosing systems behavior in large computing systems, requires a high amount of storage capacity. Sensitive data in system logs raise significant concerns about their sharing and publishing. The use of anonymization methods to cleanse sensitive data in system logs before publication reduces the usability of anonymized system logs for further analysis. After a certain level of anonymization, the cleansed system logs lose their semantic and only remain useful for certain statistical analyses. In this work, we address this tradeoff between anonymization and the usefulness of anonymized system logs. This way, full system logs anonymization is guaranteed, minimum storage space is required, and the cleansed system logs remain usable for general statistical analyses. To address the above tradeoff: (1) All variables -of every log entry- need to be replaced with defined constant values. (2) Each log entry maps to a hash-key via a hash function that is resistant to hash-key collisions. (3) The frequency of each hash-key is calculated. (4) The hash-keys are optimized based on their frequency of appearance. Additionally, based on the hash-keys frequency, the non-informative hash-keys will be eliminated. Preliminary results of analyzing system logs from a production system via the proposed method, show up to 95% reduction in required storage capacity, while the precision of the statistical analysis remains unchanged and full anonymity is guaranteed.

Event Pattern Identification in Anonymized System Logs

In this work, we address the problem of the scalability of divisible load scheduling of data parallel workloads (also called arbitrarily divisible workloads) on high performance parallel and distributed computing systems Divisible load theory offers a linear, deterministic, and tractable model for scheduling arbitrarily divisible workloads, often encountered in scientific applications With the continuous increasing problem and the system sizes, it is imperative that application scheduling algorithms scale well to leverage the processing capabilities of the high performance computing systems We conduct an analytical evaluation as well as a simulation-based study of the scalability of divisible load scheduling algorithms, called DLT algorithms, when applied for scheduling two NAS parallel benchmarks, namely, the embarrassingly parallel (EP) and the integer sort (IS) benchmarks onto a target system modeled as a 3-d torus topology The EP benchmark is computationally intensive and the IS benchmark is communication intensive Two questions related to the scalability study are addressed, namely, the fastest time to solve the problem and the condition for cost-optimality A simulation-based study was conducted to support and verify the solutions obtained via the analytical model

Scalability Analysis and Evaluation of Divisible Load Scheduling

Applications running on HPC systems waste time and energy if they: (a) use resources inefficiently, (b) deviate from allocation purpose (e.g. cryptocurrency mining), or (c) encounter errors and failures. It is important to know which applications are running on the system, how they use the system, and whether they have been executed before. To recognize known applications during execution on a noisy system, we draw inspiration from the way Shazam recognizes known songs playing in a crowded bar. Our contribution is an Execution Fingerprint Dictionary (EFD) that stores execution fingerprints of system metrics (keys) linked to application and input size information (values) as key-value pairs for application recognition. Related work often relies on extensive system monitoring (many system metrics collected over large time windows) and employs machine learning methods to identify applications. Our solution only uses the first 2 minutes and a single system metric to achieve F-scores above 95 percent, providing comparable results to related work but with a fraction of the necessary data and a straightforward mechanism of recognition.

An Execution Fingerprint Dictionary for HPC Application Recognition

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