當代的非揮發式記憶體被廣泛認為是靜態隨機存取記憶體、動態隨機存取記憶體及機械式硬碟等傳統記憶／儲存媒體的高能效替代品。在各種常見的非揮發式記憶體中，斯格明子賽道記憶體以其納秒級的存取性能、超高的存儲密度、以及對隨機插入／刪除操作的支援而聞名。然而，斯格明子賽道記憶體須依靠耗時的移位操作來對齊資料位元與存取埠以讀取或寫入資料，可能會顯著降低存取性能。另一方面，移位操作也可能會造成位置誤差和資料表示問題，因此如何提升資料的可靠性，也成為重要的設計議題。

由於斯格明子賽道記憶體獨特的特性，既有為傳統記憶／儲存媒體設計的演算法在斯格明子賽道記憶體上可能導致嚴重的性能下降。有鑑於此，既有演算法應根據斯格明子賽道記憶體之特性重新設計，以期充分發揮斯格明子賽道記憶體的潛力。具體而言，許多既有演算法傾向於以隨機跳躍的方式存取記憶體中的資料，這會產生許多耗時的移位操作。以故，如何消除斯格明子賽道記憶體不必要的移位操作以提高演算法的性能至關重要。在許多現代應用中，例如多媒體和數據分析，處理多個陣列／向量並執行某些計算任務是種常見的操作。在陣列／向量中，適當的資料放置策略對於避免斯格明子賽道記憶體不必要的移位操作至關重要。因此，在本論文的第一部分中，我們提出了一種遞迴、背靠背的資料配置方式，以最小化移位操作並改善斯格明子賽道記憶體之存取性能。同時，我們以經典的排序演算法為例，提出了一種考慮斯格明子賽道記憶體特性的排序演算法，稱為移位限制排序演算法，以展示遞迴、背靠背資料配置方式之性能優勢。

除了存取性能外，資料可靠性是設計記憶體或存儲系統時必須考慮的另一個關鍵問題。儘管既有的糾錯碼可用以檢測和糾正位元錯誤，但它們耗時的編、解碼過程往往會嚴重拖慢斯格明子賽道記憶體的效能。藉由觀察資料可靠性、存取性能和空間利用率間相互制衡的關係，在本論文的第二部分中，我們提出了一個考量資料存取粒度之斯格明子賽道記憶體管理框架，希望能在消除由位置誤差和資料表示問題所導致錯誤的同時，最佳化資料之存取性能和記憶體之空間利用率。為實現此目標，我們所提出的框架為不同存取粒度的資料，選擇不同的資料編碼、布置、索引、存取埠選擇策略，以最大限度地減少資料訪問的移位開銷。

Modern nonvolatile memories (NVMs) are widely recognized as energy-efficient replacements of classical memory/storage media, such as SRAM, DRAM, and mechanical hard disk. Among the popular NVMs, the skyrmion racetrack memory (SK-RM) is well known for its nanosecond-level access performance, ultra-high storage density, and unique supports of random data insert/delete operations. However, SK-RM relies on time-consuming shift operations to align a data bit with any access port to read or write the bit, and the access performance might degrade. On the other hand, shift operations also introduce unique issues, such as the position error and the data representation problem, which considerably impact the data reliability.

The existing algorithms designed for classical media might experience serious performance degradation when working on the SK-RM, due to the distinct characteristics of SK-RM. Thus, to fully unleash the potentials of the SK-RM, the existing algorithms should be redesigned with these SK-RM characteristics. In particular, many existing algorithms tend to access the in-memory data in a random-hopping fashion, which generates many shift operations of SK-RM. It is therefore crucial for the existing algorithms to eliminate unnecessary shift operations of SK-RM to boost the performance of the algorithms. In many modern applications, such as multimedia and data analysis, it is a common operation to process two or more arrays/vectors of data to perform certain computation tasks. In the arrays/vectors, an appropriate data placement strategy is critical for avoiding unnecessary shift operations of SK-RM. The observation thus motivates the first part of this dissertation in proposing a recursive back-to-back data placement manner to minimize the shift operations. To evaluate the back-to-back data placement manner, we take sorting algorithms as a case study, and propose a novel shift-limited sorting algorithm for SK-RM.

Besides the access performance, the data reliability is the other key issue that must be well considered when designing a memory or storage system. Although brilliant error correction codes have been proposed to detect and correct bit errors, however, their time-consuming encoding and decoding processes cannot fully match the nanosecond-level access latency of SK-RM. Observing the dilemma among data reliability, access performance, and space utilization, in the second part of this dissertation, we propose a granularity-driven management scheme for SK-RM. While eliminating the errors incurred due to the position error and the data representation problem, the proposed management scheme aims to jointly optimize access performance and space utilization. To achieve this goal, the proposed scheme adaptively selects different combinations of data encoding, layout, and indexing schemes for the data of different granularities. Moreover, we investigate the port selection problem under our proposed data layouts to minimize shift overheads on data accesses.

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