Amount Of Available Memory Research Articles

Combining Global Code and Data Compaction

Computers are increasingly being incorporated in devices with a limited amount of available memory. As a result research is increasingly focusing on the automated reduction of program size. Existing literature focuses on either data or code compaction or on highly language dependent techniques. This paper shows how combined code and data compaction can be achieved using a link-time code compaction system that reasons about the use of both code and data addresses. The analyses proposed rely only on fundamental properties of linked code and are therefore generally applicable. The combined code and data compaction is implemented in SQUEEZE, a link-time program compaction system, and evaluated on SPEC2000, MediaBench and C++ programs, resulting in total binary program size reductions of 23.6%-46.6%. This compaction involves no speed trade-off, as the compacted programs are on average about 8% faster.

Memory reduction for HDTV decoders

In this paper, we propose a low-cost memory reduction scheme which can reduce the memory requirement for full HDTV decoding from 12 MB to 4 MB. This scheme uses a switchable architecture that allows HDTV decoding with scalable memory reduction ratios. Depending on the input format and the amount of available memory, the scheme performs 1/4 or 1/2 memory reduction or no memory reduction. The 1/2 memory reduction is achieved by performing a block-based Hadamard transform followed by appropriate scalar quantization. The Hadamard transform has good signal energy compaction and a low computational cost. Appropriately designed nonuniform scalar quantizers take advantage of the statistics of the Hadamard transform coefficients and compress the data to match the 1/2 memory reduction target. The 1/4 memory reduction is achieved by 1/2 horizontal decimation followed by the 1/2 memory reduction scheme. Experimental results show that the proposed memory reduction schemes achieve good performance at very low computational cost, which makes them very attractive for digital TV applications.

Clustering and knowledge discovery in spatial databases

In the past decades, clustering has been widely used in areas such as pattern recognition, data analysis, and image processing. Recently, clustering has been recognized as a useful method for knowledge discovery in spatial databases. To efficiently detect clusters from large spatial databases with a limited amount of available memory, special database techniques have been developed. In this article, we present a survey of these methods from a database perspective.

The Effect of Memory Capacity on Time Warp Performance

The behavior of n interacting processes synchronized by the "Time Warp" rollback mechanism is analyzed under the constraint that the total amount of memory to execute the program is limited. In Time Warp, a protocol called "cancelback" has been proposed to reclaim storage when the system runs out of memory. A discrete state, continuous time Markov chain model for Time Warp augmented with the cancelback protocol is developed for a shared memory system with n homogeneous processors and homogeneous workload with constant message population. The model allows one to predict speedup as the amount of available memory is varied. The performance predicted by the model is validated through performance measurements on an operational Time Warp system executing on a shared-memory multiprocessor using a workload similar to that in the model. It is observed that if the sequential simulation requires m message buffers, Time Warp with a small fraction of message buffers beyond m performs almost as well as Time Warp with unlimited memory.

Performance analysis of “Time Warp” with limited memory

The behavior of n interacting processes synchronized by the “Time Warp” rollback mechanism is analyzed under the constraint that the total amount of memory to execute the program is limited. In Time Warp, a protocol called “cancelback” has been proposed to reclaim storage when the system runs out of memory. A discrete state, continuous time Markov chain model for Time Warp augmented with the cancelback protocol is developed for a shared memory system with n homogeneous processors and homogeneous workload. The model allows one to predict speedup as the amount of available memory is varied. To our knowledge, this is the first model to achieve this result. The performance predicted by the model is validated through direct performance measurements on an operational Time Warp system executing on a shared-memory multiprocessor using a workload similar to that in the model. It is observed that Time Warp with only a few additional message buffers per processor over that required in the corresponding sequential execution can achieve approximately the same or even greater performance than Time Warp with unlimited memory, if GVT computation and fossil collection can be efficiently implemented.

Stonn: A neural network IC using stochastic techniques: [formula omitted], and [formula omitted]. c/o Jim Symon, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599-3175 USA

The approximate pattern matching problem (APM) consists in locating all occurrences of a given pattern P in a text T allowing a specific amount of errors. Due to the character of real applications and the fact that solutions do not have error-free nature in Computer Science, solving APM is crucial for developing meaningful applications. Recently, Ilie, Navarro and Tinta presented a fast algorithm to solve APM based on the well-known Landau-Vishkin algorithm. However, the amount of available memory limits the usage of their algorithm, since it requires all the answer array be in memory. In this article, a practical semi-external memory method to solve APM is presented. The method is based on the direct-comparison variation from Ilie et al.'s algorithm. Performance tests with real data of length up to 1.2 GB showed that the presented method is about 5 times more space-efficient than Ilie et al.'s algorithm and yet, has a competitive trade-off regarding time.