• Vacuum Magazine
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• v.1 no.3
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• pp.4-8
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• 2014

Since the modern computer architecture was suggested by Von Neumann in 1945, computer has become inevitable for our life. This brilliant growth of computer has been led by device miniaturization trend, so called Moore's law. Especially, the explosive growth of memory devices such as DRAM and Flash have played key role in huge enlarging utilization of computer. However, abrupt increase of data used for many applications in big data era provoke the excessive energy consumption of data center which results from the inefficiency of conventional memory-storage hierarchy. As a solution, the application of new memory devices has been brought up for innovative memory-storage hierarchy. In this paper, the current development status and prospect of new memory devices will be discussed.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.9
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• pp.445-458
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• 2007

This article presents a novel infrastructure for large-memory database processing using commodity hardware with operating system support. We exploit inexpensive PCs and a high-speed network capable of Remote Direct Memory Access (RDMA) operations to build a new memory hierarchy between fast volatile memory and slow disk storage. The new memory hierarchy guarantees a reasonable response time, and its storage size enables us to run large-memory database systems with little performance degradation. The proposed architecture has two main components: (1) a remote memory system inside the Linux kernel to manage other computers' memory pages efficiently and (2) a remote memory pager responsible for manipulating remote read/write operations on remote memory pages. We insist that the proposed architecture is practical enough to support the rigorous demands of commercial in-memory database systems by demonstrating the performance of publicly available main-memory databases (e.g., MySQL) on our prototyped system. The experimental results show very interesting results from the TPC-C benchmark.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.78-83
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• 2023

This paper proposes a lightweight trace-driven Processing-In-Memory (PIM) simulator, TP-Sim. TP-Sim is a General Purpose PIM (GP-PIM) simulator that evaluates various PIM system performance-related metrics. Based on instruction and memory traces extracted from the Intel Pin tool, TP-Sim can replay trace files for multiple models of PIM architectures to compare its performance. To verify the availability of TP-Sim, we estimated three different system configurations on the STREAM benchmark. Compared to the traditional Host CPU-only systems with conventional memory hierarchy, simple GP-PIM architecture achieved better performance; even the Host CPU has the same number of in-order cores. For further study, we also extend TP-Sim as a part of a heterogeneous system simulator that contains CPU, GPGPU, and PIM as its primary and co-processors.

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.6
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• pp.345-359
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• 2011

In high performance embedded systems, the use of multiple on-chip memories is an essential architectural feature for exploiting inherent parallelism in multimedia applications. This feature allows multiple data accesses to be executed in parallel. However, it remains difficult to effectively exploit of multiple on-chip memories. The successful use of this architecture strongly depends on how to efficiently detect and exploit memory parallelism in target applications. In this paper, we propose a technique based on a linear array access descriptor [1], which is generated from profiled data, to detect and exploit memory parallelism. The proposed technique tackles an array reorganization problem to maximize memory parallelism in multimedia applications. We present preliminary experiments applying the proposed technique onto a representative coarse grained reconfigurable array processor (CGRA) with multimedia kernel codes. Our experimental results demonstrate that our technique optimizes data placement by putting independent data on separate storage. The results exhibit 9.8% higher performance on average compared to the existing method.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.142-148
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• 2023

In this paper, we identify performance issues in executing compute kernels from PolyBench, which includes compute kernels that are the core computational units of various data-intensive workloads, such as deep learning and data-intensive applications, on Processing-in-Memory (PIM) devices. Therefore, using our in-house simulator, we measured and compared the various performance metrics of workloads based on traditional out-of-order and in-order processors with Processing-in-Memory-based systems. As a result, the PIM-based system improves performance compared to other computing models due to the short-term data reuse characteristic of computational kernels from PolyBench. However, some kernels perform poorly in PIM-based systems without a multi-layer cache hierarchy due to some kernel's long-term data reuse characteristics. Hence, our evaluation and analysis results suggest that further research should consider dynamic and workload pattern adaptive approaches to overcome performance degradation from computational kernels with long-term data reuse characteristics and hidden data locality.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.4
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• pp.6-11
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• 2018

As embedded memory technology evolves, the traditional Static Random Access Memory (SRAM) technology has reached the end of development. For deepening the manufacturing process technology, the next generation memory technology is highly required because of the exponentially increasing leakage current of SRAM. Non-volatile memories such as STT-MRAM (Spin Torque Transfer Magnetic Random Access Memory), PCM (Phase Change Memory) are good candidates for replacing SRAM technology in embedded memory systems. They have many advanced characteristics in the perspective of power consumption, leakage power, size (density) and latency. Nonetheless, nonvolatile memories have two major problems that hinder their use it the next-generation memory. First, the lifetime of the nonvolatile memory cell is limited by the number of write operations. Next, the write operation consumes more latency and power than the same size of the read operation.These disadvantages can be solved using the compiler. The disadvantage of non-volatile memory is in write operations. Therefore, when the compiler decides the layout of the data, it is solved by optimizing the write operation to allocate a lot of data to the SRAM. This study provides insights into how these compiler and architectural designs can be developed.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.11_12
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• pp.608-616
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• 2005

A cache architecture is proposed here which evokes prefetch at level 1 cache miss. Existing structures only prefetch at level 2 cache miss. In the proposed cache architecture, level 1 cache miss would select demand fetch block and prefetch block from the level 2 cache and store to level 1 cache and prefetch cache, respectively. According to an experimental analysis using 11 benchmark programs, the hierarchical cache architecture that employs both a level 1 cache prefetcher and a level 2 cache prefetcher obtained a maximum $19\%$ increased performance when compared to the cache architecture that employs only a level 2 cache prefetcher.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.100-106
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• 2017

In this paper, we propose a parallel optimization method of Aho-Corasick (AC) algorithm and Parallel Failureless Aho-Corasick (PFAC) algorithm using Open Computing Language (OpenCL) on Field Programmable Gate Array (FPGA). The low throughput of string matching engine causes the performance degradation of network process. Recently, many researchers have studied the string matching engine using parallel computing. FPGA's vendors offer a parallel computing platform using OpenCL. In this paper, we apply the AC and PFAC algorithm on DE1-SoC board with Cyclone V FPGA, where the optimization that considers FPGA architecture is performed. Experiments are performed considering global id, local id, local memory, and loop unrolling optimizations using PFAC algorithm. The performance improvement using loop unrolling is 129 times greater than AC algorithm that not adopt loop unrolling. The performance improvements using loop unrolling are 1.1, 0.2, and 1.5 times greater than those using global id, local id, and local memory optimizations mentioned above.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.7
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• pp.775-787
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• 2010

Unlike general-purpose CPUs, the GPUs have been specialized as many-core streaming processors, and are frequently replacing the CPUs in an increasing range of computations thanks to their outstanding parallel computing capacity. In order to respond to such trend, NVIDIA has recently issued a new parallel computing architecture called CUDA(Compute Unified Device Architecture), offering a flexible GPU programming environment for GPGPU(General Purpose GPU) computing. In general, when programmers use the CUDA API, they should clearly understand many aspects of GPU's computing architecture to produce efficient parallel software. In this article, we explain several optimization techniques for CUDA programming that we have verified through a lot of experiment and trial and error, and review how those techniques affect the performance of code execution. In particular, we use a specific problem as an example to analyze several elements that affect performances, such as effective accesses to hierarchical memory system, processor occupancy, and latency hiding. In conclusion, we present several directions that may be utilized effectively in CUDA-based parallel programming.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.9
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• pp.461-476
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• 2003

Due to the implementation ease and alleviation of memory bottleneck effect, NUMA architecture has dominated in the multiprocessor systems for the past several years. However, because the NUMA system distributes memory in each node, frequent remote memory access is a key factor of performance degradation. Therefore, efficient design of RAC(Remote Access Cache) in NUMA system is critical for performance improvement. In this paper, we suggest Multi-Grain RAC which can adaptively control the RAC line size, with respect to each application behavior Then we simulate NUMA system with multi-grain RAC using MINT, event-driven memory hierarchy simulator. and analyze the performance results. At first, with profile-based determination method, we verify the optimal RAC line size for each application and, then, we compare and analyze the performance differences among NUMA systems with normal RAC, with optimal line size RAC, and with multi-grain RAC. The simulation shows that the worst case can be always avoided and results are very close to optimal case with any combination of application and RAC format.