Parallel programs that use critical sections and are executed on a shared-memory multiprocessor with a writeinvalidate protocol result in invalidation actions that could be eliminated.For this type of sharing, called m"gratory sharing, each processor typically causes a cache miss followed by an invalidation request which could be merged with the preceding cache-muss request.In this paper we propose an adaptive protocol that invokes this optimization dynamically for n'gratory blocks. For other blocks, the protocol works as an ordinary write-invalidate protocol. We show that the protocol is a simple extension to a write-invalidate protocol. Based on a program-driven simulation model of an architecture sim"lar to the Stanford DASH, and a set of four benchmarks, we evaluate the potential performance improvements of the protocol. We jind that it effectively eliminates most single invalidations which improves the performance by reducing the shared access penalty and the network trajic.

No abstract

No abstract

No abstract

In last decade, data analytics have rapidly progressed from traditional disk-based processing to modern inmemory processing. However, little effort has been devoted at enhancing performance at micro-architecture level. This paper characterizes the performance of in-memory data analytics using Apache Spark framework. We use a single node NUMA machine and identify the bottlenecks hampering the scalability of workloads. We also quantify the inefficiencies at micro-architecture level for various data analysis workloads. Through empirical evaluation, we show that spark workloads do not scale linearly beyond twelve threads, due to work time inflation and thread level load imbalance. Further, at the micro-architecture level, we observe memory bound latency to be the major cause of work time inflation.

SUMMARYWe describe here the design and performance of OdinMP/CCp, which is a portable compiler for C-programs using the OpenMP directives for parallel processing with shared memory. OdinMP/CCp was written in Java for portability reasons and takes a C-program with OpenMP directives and produces a C-program for POSIX threads.We describe some of the ideas behind the design of OdinMP/CCp and show some performance results achieved on an SGI Origin 2000 and a Sun E10000. Speedup measurements relative to a sequential version of the test programs show that OpenMP programs using OdinMP/CCp exhibit excellent performance on the Sun E10000 and reasonable performance on the Origin 2000.

Performance degradation due to nonuniform data access latencies has worsened on NUMA systems and can now be felt on-chip in manycore processors. Distributing data across NUMA nodes and manycore processor caches is necessary to reduce the impact of nonuniform latencies. However, techniques for distributing data are error-prone and fragile and require low-level architectural knowledge. Existing task scheduling policies favor quick load-balancing at the expense of locality and ignore NUMA node/manycore cache access latencies while scheduling. Locality-aware scheduling, in conjunction with or as a replacement for existing scheduling, is necessary to minimize NUMA effects and sustain performance. We present a data distribution and locality-aware scheduling technique for task-based OpenMP programs executing on NUMA systems and manycore processors. Our technique relieves the programmer from thinking of NUMA system/manycore processor architecture details by delegating data distribution to the runtime system and uses task data dependence information to guide the scheduling of OpenMP tasks to reduce data stall times. We demonstrate our technique on a four-socket AMD Opteron machine with eight NUMA nodes and on the TILEPro64 processor and identify that data distribution and locality-aware task scheduling improve performance up to 69% for scientific benchmarks compared to default policies and yet provide an architecture-oblivious approach for programmers.

Abstract. Online advertising is increasingly switching to real-time bidding on advertisement inventory, in which the ad slots are sold through real-time auctions upon users visiting websites or using mobile apps. To compete with unknown bidders in such a highly stochastic environment, each bidder is required to estimate the value of each impression and to set a competitive bid price. Previous bidding algorithms have done so without considering the constraint of budget limits, which we address in this paper. We model the bidding process as a Constrained Markov Decision Process based reinforcement learning framework. Our model uses the predicted click-through-rate as the state, bid price as the action, and ad clicks as the reward. We propose a bidding function, which outperforms the state-of-the-art bidding functions in terms of the number of clicks when the budget limit is low. We further simulate different bidding functions competing in the same environment and report the performances of the bidding strategies when required to adapt to a dynamic environment.