TechTalks from event: IEEE IPDPS 2011

Note 1: Only plenary sessions (keynotes, panels, and best papers) are accessible without requiring log-in. For other talks, you will need to log-in using the email you registered for IPDPS 2011. Note 2: Many of the talks (those without a thumbnail next to the their description below) are yet to be uploaded. Some of them were not recorded because of technical problems. We are working with the corresponding authors to upload the self-recorded versions here. We sincerely thank all authors for their efforts in making their videos available.

Intel Platinum Patron Night

  • Architecting Parallel Software: Design patterns in practice and teaching Authors: Michael Wrinn, Intel
    Design patterns can systematically identify reusable elements in software engineering, and have been particularly effective in codifying practice in object-oriented software. A team of researchers centered at UC Berkeley’s Parallel Computing Laboratory continues to investigate a design pattern approach to parallel software; the effort has matured to the point that an undergraduate course was delivered on the topic in Fall 2010. This talk will briefly describe the pattern language itself, then demonstrate its application in examples from both image processing and game design.
  • Teaching Parallelism Using Games Authors: Ashish Amresh, Intel; Amit Jindal, Intel
    Academic institutions do not have to spend expensive multi-core hardware to support game-based courses to teach parallelism. We will discuss what teaching methodologies educators can use for integrating parallel computing curriculum inside a game engine. We will talk about the full game development process, from game design to game engineering and how parallelism is critical. We will show five game demos that mirror current trends in the industry and how educators can use in these games in the classroom. We will also show the learning outcomes, what parallelism topics are appropriate to teach students at various levels. We will demonstrate how to take games running serially and modify them to run parallel.
  • Starting Your Future Career at Intel Authors: Dani Napier, Intel; Lauren Dankiewicz, Intel
    Intel's Dani Napier will introduce why Intel is a great place to work-- it's challenging, has great benefits and is abundant with rewarding growth opportunities. She will expand on why parallelism is crucial to Intel's growth strategy and give an overview of the various types of jobs in which knowledge of parallel and distributed processing apply at Intel. Finally, Dani will explain the new hire development process and why Intel is the company that will help you become successful in your desired career path. Lauren Dankiewicz will discuss her background from the University of California, Berkeley. She gives an insightful and humorous commentary on the interview process at Intel, drawing similarities to dating. Lauren describes the excitement, the uncertainty, and what it takes to make the right choice! Listen to this fun and engaging real-life clip of how an intern became a full-time employee at Intel.
  • Opening Remarks Authors:
    Intel Platinum Patron Night will be held on Thursday evening, 5:30-8:30pm, in the Kuskokwim Ballroom. This will be an exciting opportunity for IPDPS attendees to network and learn about the Intel Academic Community’s free resources to support parallel computing research and teaching. Intel recruiters will share information about engineering internships and careers for recent college graduates.

25th Year IPDPS Celebration

SESSION 1: Resource Management

  • Power-aware replica placement and update strategies in tree networks Authors: Anne Benoit (ENS Lyon, France); Paul Renaud-Goud (LIP, ENS Lyon, France); Yves Robert (ENS Lyon, France)
    This paper deals with optimal strategies to place replicas in tree networks, with the double objective to minimize the total cost of the servers, and/or to optimize power consumption. The client requests are known beforehand, and some servers are assumed to pre-exist in the tree. Without power consumption constraints, the total cost is an arbitrary function of the number of existing servers that are reused, and of the number of new servers. Whenever creating and operating a new server has higher cost than reusing an existing one (which is a very natural assumption), cost optimal strategies have to trade-off between reusing resources and load-balancing requests on new servers. We provide an optimal dynamic programming algorithm that returns the optimal cost, thereby extending known results without pre-existing servers. With power consumption constraints, we assume that servers operate under a set of M different modes depending upon the number of requests that they have to process. In practice M is a small number, typically 2 or 3, depending upon the number of allowed voltages. Power consumption includes a static part, proportional to the total number of servers, and a dynamic part, proportional to a constant exponent of the server mode, which depends upon the model for power. The cost function becomes a more complicated function that takes into account reuse and creation as before, but also upgrading or downgrading an existing server from one mode to another. We show that with an arbitrary number of modes, the power minimization problem is NP-complete, even without cost constraint, and without static power. Still, we provide an optimal dynamic programming algorithm that returns the minimal power, given a threshold value on the total cost; it has exponential complexity in the number of modes M, and its practical usefulness is limited to small values of M. Still, experiments conducted with this algorithm show that it can process large trees in reasonable time, despite its worst-case complexity.
  • Minimum Cost Resource Allocation for meeting job requirements Authors: Venkatesan T Chakaravarthy (IBM Research (India), India); Sambuddha Roy (IBM Research - India, India); Yogish Sabharwal (IBM Re
    We consider the problem of allocating resources for completing a collection of jobs. Each resource is speci?ed by a start-time, ?nish-time and the capacity of resource available and has an associated cost; and each job is speci?ed by a starttime, ?nish-time and the amount of the resource required (demand) during this interval. A feasible solution is a multiset of resources (i.e., multiple units of each resource may be picked) such that at any point of time, the sum of the capacities offered by the resources is at least the total demand of the jobs active at that point of time. The cost of the solution is the sum of the costs of the resources included in the solution (taking into account the units of the resources). The goal is to ?nd a feasible solution of minimum cost. This problem arises naturally in many scenarios. For example, given a set of jobs, we would like to allocate some resource such as machines, memory or bandwidth in order to complete all the jobs. This problem generalizes a covering version of the knapsack problem which is known to be NP-hard. We present a constant factor approximation algorithm for this problem based on a Primal-Dual approach.
  • Power and Performance Management in Priority-type Cluster Computing Systems Authors: Kaiqi Xiong (North Carolina State University, USA)
    Cluster computing not only improves performance but also increase power consumption. It is a challenge to increase the performance of a cluster computing system and reduce its power consumption simultaneously. In this paper, we consider a collection of cluster computing resources owned by a service provider to host an enterprise application for multiple class business customers where customer requests are distinguished, with different request characteristics and service requirements. We start with a development of computing an average end-to-end delay and an average energy consumption for multiple class customers in such an application. Then, we present approaches for optimizing the average end-to-end delay subject to the constraint of an average energy consumption and optimizing the average end-to-end energy consumption subject to the constraints of an average end-to-end delay for all class and each class customer requests respectively. Moreover, a service provider processes the service requests of customers according to a service level agreement (SLA), which is a contract agreed between a customer and a service provider. It becomes important and commonplace to prioritize multiple customer services in favor of customers who are willing to pay higher fees. We propose an approach for minimizing the total cost of cluster computing resources allocated to ensure multiple priority customer service guarantees by the service provider. It is demonstrated through our simulation that the proposed approaches are ef?cient and accurate for power management and performance guarantees in priority-type cluster computing systems
  • Willow: A Control System For Energy And Thermal Adaptive Computing Authors: Krishna Kant (National Science Foundation, USA); Muthukumar Murugan (University of Minnesota, USA); David Du (University of Min
    The increasing energy demand coupled with emerging sustainability concerns requires a re-examination of power/thermal issues in data centers from the perspective of short term energy de?ciencies. Such energy de?cient scenarios arise for a variety of reasons including variable energy supply from renewable sources and inadequate power, thermal and cooling capacities. In this paper we propose a hierarchical control scheme to adapt assignments of tasks to servers in a way that can cope with the varying energy limitations and still provide necessary QoS . The rescheduling of tasks on different servers has direct (migration related) and indirect (changed traf?c patterns) network energy impacts that we also consider. We show the stability of our scheme and evaluate its performance via detailed simulations and experiments.