17099847
Dynamic Scratch-Pad Memory Management for Irregular Array Access Patterns
There exist many embedded applications such as those executing on set-top boxes, wireless base stations, HDTV, and mobile handsets that are structured as nested loops and benefit significantly from software managed memory. Prior work on scratchpad memories (SPMs) focused primarily on applications with regular data access patterns. Unfortunately, some embedded applications do not fit in this category and consequently conventional SPM management schemes will fail to produce the best results for them. In this work, we propose a novel compilation strategy for data SPMs for embedded applications that exhibit irregular data access patterns. Our scheme divides the task of optimization between compiler and runtime. The compiler processes each loop nest and inserts code to collect information at runtime. Then, the code is modified in such a fashion that, depending on the collected information, it dynamically chooses to use or not to use the data SPM for a given set of accesses to irregular arrays. Our results indicate that this approach is very successful with the applications that have irregular patterns and improves their execution cycles by about 54% over a state-of-the-art SPM management technique and 23% over the conventional cache memories. Also, the additional code size overhead incurred by our approach is less than 5% for all the applications tested
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.
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.
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.
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.
316842
Cycles in 4-connected planar graphs
Let G be a 4-connected planar graph on n vertices. Previous results show that G contains a cycle of length k for each k ∈ {n, n - 1,n - 2, n - 3} with k ≥ 3. These results are proved using the "Tutte path" technique, and this technique alone cannot be used to obtain further results in this direction. One approach to obtain further results is to combine Tutte paths and contractible edges. In this paper, we demonstrate this approach by showing that G also has a cycle of length k for each k ∈ {n - 4, n - 5, n - 6} with k ≥ 3. This work was partially motivated by an old conjecture of Malkevitch.
35779178
On the Quickest Path Problem
In this paper, we propose an O(mn2) time algorithm that finds all-pairs quickest paths in a given network N, where n and m are the numbers of nodes and arcs, respectively, in N. Besides, the quickest path between any two nodes can be determined in O(log m) time, provided O(mn2) time preprocessing is made.
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.
6873995
Policy-driven data dissemination for context-aware applications
Context-aware pervasive-computing applications require continuous monitoring of their physical and computational environment to make appropriate adaptation decisions in time. The data streams produced by sensors, however, may overflow the queues on the dissemination path. Traditional flow-control and congestion-control policies either drop data or force the sender to pause. When the data sender is sensing the physical environment, however, a pause is equivalent to dropping data. Instead of arbitrarily dropping data that may contain important events, we present a policy-driven data dissemination service named PACK, based on an overlay-based infrastructure for efficient multicast delivery. PACK enforces application-specified policies that define how to discard or summarize data flows wherever queues overflow on the data path, notably at the mobile hosts where applications often reside. A key contribution of our approach is to uniformly apply the data-stream "packing" abstraction to queue overflow caused by network congestion, slow receivers, and temporary disconnection. We present experimental results and a detailed application study of the PACK service.