Compilation, architectural support, and evaluation of SIMD graphics pipeline programs on a general-purpose CPU
Graphics and media processing is quickly emerging to become one of the key computing workloads. Programmable graphics processors give designers extra flexibility by running a small program for each fragment in the graphics pipeline. We investigate low-cost mechanisms to obtain good performance for modern graphics programs on a general purpose CPU. We present a compiler that compiles SIMD graphics program and generates efficient code on a general purpose CPU. The generated code can process between 25-0.3 million vertices per second on a 2.2 GHz Intel Pentium/spl reg/ 4 processor for a group of typical graphics programs. We also evaluate the impact of three changes in the architecture and compiler. Adding support for new specialized instructions improves the performance of the programs by 27.4% on average. A novel compiler optimization called mask analysis improves the performance of the programs by 19.5% on average. Increasing the number of architectural SIMD registers from 8 to 16 registers significantly reduces the number of memory accesses due to register spills.

title: Carbon: architectural support for fine-grained parallelism on chip multiprocessors
abstract: Chip multiprocessors (CMPs) are now commonplace, and the number of cores on a CMP is likely to grow steadily. However, in order to harness the additional compute resources of a CMP, applications must expose their thread-level parallelism to the hardware. One common approach to doing this is to decompose a program into parallel "tasks" and allow an underlying software layer to schedule these tasks to different threads. Software task scheduling can provide good parallel performance as long as tasks are large compared to the software overheads.
 We examine a set of applications from an important emerging domain: Recognition, Mining, and Synthesis (RMS). Many RMS applications are compute-intensive and have abundant thread-level parallelism, and are therefore good targets for running on a CMP. However, a significant number have small tasks for which software task schedulers achieve only limited parallel speedups.
 We propose Carbon, a hardware technique to accelerate dynamic task scheduling on scalable CMPs. Carbon has relatively simple hardware, most of which can be placed far from the cores. We compare Carbon to some highly tuned software task schedulers for a set of RMS benchmarks with small tasks. Carbon delivers significant performance improvements over the best software scheduler: on average for 64 cores, 68% faster on a set of loop-parallel benchmarks, and 109% faster on aset of task-parallel benchmarks.

title: Dynamic memory management for programmable devices

title: The adaptive agent architecture: achieving fault-tolerance using persistent broker teams
abstract: Brokered multi-agent systems can be incapacitated and rendered non-functional when the brokers become inaccessible due to failures that can occur in any distributed software system. We propose that the theory of teamwork can be used to specify robust brokered architectures that can recover from broker failures, and we present the adaptive agent architecture (AAA) to show the feasibility of this approach. The previous teamwork theory based on joint intentions assumes that team members remain in a team as long as the team exists. We extend this theory to allow dynamic broker teams whose members can change with time. We also introduce a theory of restorative maintenance goals that enables the brokers in an AAA broker team to start new brokers and recruit them to the broker team. As a result, an AAA-based multi-agent system can maintain a specified number of functional brokers in the system despite broker failures, thus effectively becoming a self-healing system.

title: Representing and executing protocols as joint actions
abstract: Families of conversation protocols can be expressed formally as partially ordered landmarks where the landmarks represent the state of affairs that must be brought about during the goal-directed execution of a protocol. Then, concrete protocols represented as joint action expressions can be derived from the partially ordered landmarks and executed directly by joint intention interpreters, thus nearly eliminating the need to implement a separate protocol handling system. This approach also supports (1) flexibility in the actions used to achieve landmarks, (2) shortcutting protocol execution, and (3) application of the joint intention theory to provide automatic exception handling along with a correctness criterion for protocols.

title: Discriminative Fields for Modeling Spatial Dependencies in Natural Images
abstract: In this paper we present Discriminative Random Fields (DRF), a discriminative framework for the classification of natural image regions by incorporating neighborhood spatial dependencies in the labels as well as the observed data. The proposed model exploits local discriminative models and allows to relax the assumption of conditional independence of the observed data given the labels, commonly used in the Markov Random Field (MRF) framework. The parameters of the DRF model are learned using penalized maximum pseudo-likelihood method. Furthermore, the form of the DRF model allows the MAP inference for binary classification problems using the graph min-cut algorithms. The performance of the model was verified on the synthetic as well as the real-world images. The DRF model outperforms the MRF model in the experiments.

title: Exploiting Inference for Approximate Parameter Learning in Discriminative Fields: An Empirical Study
abstract: Estimation of parameters of random field models from labeled training data is crucial for their good performance in many image analysis applications. In this paper, we present an approach for approximate maximum likelihood parameter learning in discriminative field models, which is based on approximating true expectations with simple piecewise constant functions constructed using inference techniques. Gradient ascent with these updates exhibits compelling limit cycle behavior which is tied closely to the number of errors made during inference. The performance of various approximations was evaluated with different inference techniques showing that the learned parameters lead to good classification performance so long as the method used for approximating the gradient is consistent with the inference mechanism. The proposed approach is general enough to be used for the training of, e.g., smoothing parameters of conventional Markov Random Fields (MRFs).

title: A combinatorial approach for studying LOCC transformations of multipartite states
abstract: We develop graph theoretic methods for analysing maximally entangled pure states distributed between a number of different parties. We introduce a technique called {\it bicolored merging}, based on the monotonicity feature of entanglement measures, for determining combinatorial conditions that must be satisfied for any two distinct multiparticle states to be comparable under local operations and classical communication (LOCC). We present several results based on the possibility or impossibility of comparability of pure multipartite states. We show that there are exponentially many such entangled multipartite states among $n$ agents. Further, we discuss a new graph theoretic metric on a class of multi-partite states, and its implications.