857549
Exploiting frequent field values in java objects for reducing heap memory requirements
The capabilities of applications executing on embedded and mobile devices are strongly influenced by memory size limitations. In fact, memory limitations are one of the main reasons that applications run slowly or even crash in embedded/mobile devices. While improvements in technology enable the integration of more memory into embedded devices, the amount memory that can be included is also limited by cost, power consumption, and form factor considerations. Consequently, addressing memory limitations will continue to be of importance.Focusing on embedded Java environments, this paper shows how object compression can improve memory space utilization. The main idea is to make use of the observation that a small set of values tend to appear in some fields of the heap-allocated objects much more frequently than other values. Our analysis shows the existence of such frequent field values in the SpecJVM98 benchmark suite. We then propose two object compression schemes that eliminate/reduce the space occupied by the frequent field values. Our extensive experimental evaluation using a set of eight Java benchmarks shows that these schemes can reduce the minimum heap size allowing Java applications to execute without out-of-memory exceptions by up to 24% (14% on an average).
7347899
Data compression for improving SPM behavior
Scratch-pad memories (SPMs) enable fast access to time-critical data. While prior research studied both static and dynamic SPM management strategies, not being able to keep all hot data (i.e., data with high reuse) in the SPM remains the biggest problem. This paper proposes data compression to increase the number of data blocks that can be kept in the SPM. Our experiments with several embedded applications show that our compression-based SPM management heuristic is very effective and outperforms prior static and dynamic SPM management approaches. We also present an ILP formulation of the problem, and show that the proposed heuristic generates competitive results with those obtained through ILP, while spending much less time in compilation.
2552667
An evaluation of code and data optimizations in the context of disk power reduction
Disk power management is becoming increasingly important in high-end server and cluster type of environments that execute data-intensive applications. While hardware-only approaches (e.g., low-power modes supported by current disks) are successful to a certain extent, one also needs to consider the software side to achieve further energy savings. This paper first demonstrates that conventional data locality oriented code transformations are not sufficient for minimizing disk power consumption. The reason is that these optimizations do not take into account how disk-resident array data are laid out on the disk system, and consequently, fail to increase idle periods of disks, which is the primary metric using which disk power can be reduced. Instead, we propose a disk layout aware application optimization strategy that uses both code restructuring and data layout optimization. Our experimental evaluation with several benchmark codes reveal that the proposed strategy is very successful in reducing disk energy consumption without performing much worse than a pure data locality oriented scheme, as far as execution cycles are concerned. The experiments also show that the benefits coming from our approach increase with the increased number of disks; i.e., it scales very well.
17088807
Reducing NoC energy consumption through compiler-directed channel voltage scaling
While scalable NoC (Network-on-Chip) based communication architectures have clear advantages over long point-to-point communication channels, their power consumption can be very high. In contrast to most of the existing hardware-based efforts on NoC power optimization, this paper proposes a compiler-directed approach where the compiler decides the appropriate voltage/frequency levels to be used for each communication channel in the NoC. Our approach builds and operates on a novel graph based representation of a parallel program and has been implemented within an optimizing compiler and tested using 12 embedded benchmarks. Our experiments indicate that the proposed approach behaves better - from both performance and power perspectives - than a hardwarebased scheme and the energy savings it achieves are very close to the savings that could be obtained from an optimal, but hypothetical voltage/frequency scaling scheme.
17981801
Studying storage-recomputation tradeoffs in memory-constrained embedded processing
Fueled by an unprecedented desire for convenience and self-service, consumers are embracing embedded technology solutions that enhance their mobile lifestyles. Consequently, we witness an unprecedented proliferation of embedded/mobile applications. Most of the environments that execute these applications have severe power, performance, and memory space constraints that need to be accounted for. In particular, memory limitations can present serious challenges to embedded software designers. The current solutions to this problem include sophisticated packaging techniques and code optimizations for effective memory utilization. While the first solution is not scalable, the second one is restricted by intrinsic data dependences in the code that prevent code restructuring. In this paper, we explore an alternate approach for reducing memory space requirements of embedded applications. The idea is to re-compute the result of a code block (potentially multiple times) instead of storing it in memory and performing a memory operation whenever needed. The main benefit of this approach is that it reduces memory space requirements, that is, no memory space is reserved for storing the result of the code block in question.
13530819
Exploiting last idle periods of links for network power management
Network power optimization is becoming increasingly important as the sizes of the data manipulated by parallel applications and the complexity of inter-processor data communications are continuously increasing. Several hardware-based schemes have been proposed in the past for reducing network power consumption, either by turning off unused communication links or by lowering voltage/frequency in links with low usage. While the prior research shows that these schemes can be effective in certain cases, they share the common drawback of not being able to predict the link active and idle times very accurately. This paper, instead, proposes a compiler-based scheme that determines the last use of communication links at each loop nest and inserts explicit link turn-off calls in the application source. Specifically, for each loop nest, the compiler inserts a turn-off call per communication link. Each turned-off link is reactivated upon the next access to it. We automated this approach within a parallelizing compiler and applied it to eight array-intensive embedded applications.
1852937
Context-sensitive resource discovery
This paper presents the "Solar" system framework that allows resources to advertise context-sensitive names and for applications to make context-sensitive name queries. The heart of our framework is a small specification language that allows composition of "context-processing operators" to calculate the desired context. Resources use the framework to register names, and applications use the framework to look up context-sensitive name descriptions. The back-end system executes these operators and constantly updates the context values, adjusting advertised names and informing applications about changes. We report experimental results from a prototype, using a modified version of the intentional naming system (INS) as the core directory service.
206843939
The Quickest Path Problem in Distributed Computing Systems
Let N=(V, A, C, L) be an input network with node set V, arc set A, positive arc capacity function C, and nonnegative arc lead time function L. The quickest path problem is to find a path in N to transmit a given amount of data such that the transmission time is minimized. In this paper, distributed algorithms are developed for the quickest path problem in an asynchronous communication network. For the one-source quickest path problem, we present two algorithms that require O(rn2) messages, O(rn2) time, and O(rmn) messages, O(rn) time, respectively, where m=¦A¦, n=¦V ¦, and r is the number of distinct capacity values of N. For the all-pairs quickest path problem, we present an algorithm that requires O(mn) messages and O(m) time.
32173892
Note on Whitney's Theorem for k-connected Graphs
16806816
Dependency management in distributed settings
Applications in a computation and communication saturated pervasive-computing environment have to adapt their behaviors according the context of the user and the physical space. These "context-aware" applications typically obtain high-level situational understanding by aggregating one or more outputs of distributed sensors. To reduce the complexity of developing and deploying these adaptive applications, we designed and implemented a flexible software infrastructure, Solar, for large-scale context fusion (Chen et al., 2004). Solar consists of a set of functionally equivalent nodes, named Planets, which peer together to form a service overlay. A sensor connects to a Planet to register a name with Solar's naming service and publish a stream of events, while an application connects to a Planet to subscribe to one or more sensor outputs by querying Solar's name space. In addition, applications may inject data-fusion operators into the Solar to reduce the computation and communication costs on the mobile devices. In this paper we present our approach to address the dependency management issues in Solar.
43538481
Vertex-disjoint cycles containing specified vertices in a bipartite graph
A minimum degree condition is given for a bipartite graph to contain a 2-factor each component of which contains a previously specified vertex. 2004 Wiley Periodicals, Inc. J Graph Theory 46: 145–166, 2004