The storage hardware is evolving at a rapid pace to keep up with the exponential rise of data consumption. Recently, ultra-fast storage technologies such as nano-second scale byte- addressable Non-Volatile Memory (NVM), micro-second scale SSDs are being commercialized. However, the OS storage stack has not been evolving fast enough to keep up with these new ultra-fast storage hardware. Hence, the latency due user-kernel context switch caused by system calls and hardware interrupts is no longer negligible as presumed in the era of slower high latency hard disks. Further, the OS storage stack is not designed with multi-core scalability in mind; so with CPU core count continuously increasing, the OS storage stack particularly the Virtual Filesystem (VFS) and filesystem layer are increasingly becoming a scalability bottleneck. Applications bypass the kernel (kernel-bypass storage stack) completely to eliminate the storage stack from becoming a performance and scalability bottleneck. But this comes at the cost of programmability, isolation, safety, and reliability. Moreover, scalability bottlenecks in the filesystem can not be addressed by simply moving the filesystem to the userspace. Overall, while designing a kernel-bypass storage stack looks obvious and promising there are several critical challenges in the aspects of programmability, performance, scalability, safety, and reliability that needs to be addressed to bypass the traditional OS storage stack. This thesis proposes a series of kernel-bypass storage techniques designed particularly for fast memory-centric storage. First, this thesis proposes a scalable persistent transactional memory (PTM) programming model to address the programmability and multi-core scalability challenges. Next, this thesis proposes techniques to make the PTM memory safe and fault tolerant. Further, this thesis also proposes a kernel-bypass programming framework to port legacy DRAM-based in-memory database applications to run on persistent memory-centric storage. Finally, this thesis explores an application-driven approach to address the CPU side and storage side bottlenecks in the deep learning model training by proposing a kernel-bypass programming framework to move to compute closer to the storage. Overall, the techniques proposed in this thesis will be a strong foundation for the applications to adopt and exploit the emerging ultra-fast storage technologies without being bottlenecked by the traditional OS storage stack. / Doctor of Philosophy / The storage hardware is evolving at a rapid pace to keep up with the exponential rise of data consumption. Recently, ultra-fast storage technologies such as nano-second scale byte- addressable Non-Volatile Memory (NVM), micro-second scale SSDs are being commercialized. The Operating System (OS) has been the gateway for the applications to access and manage the storage hardware. Unfortunately, the OS storage stack that is designed with slower storage technologies (e.g., hard disk drives) becomes a performance, scalability, and programmability bottleneck for the emerging ultra-fast storage technologies. This has created a large gap between the storage hardware advancements and the system software support for such emerging storage technologies. Consequently, applications are constrained by the limitations of the OS storage stack when they intend to explore these emerging storage technologies. In this thesis, we propose a series of novel kernel-bypass storage stack designs to address the performance, scalability, and programmability limitations of the conventional OS storage stack. The kernel-bypass storage stack proposed in this thesis is carefully designed with ultra-fast modern storage hardware in mind. Application developers can leverage the kernel-bypass techniques proposed in this thesis to develop new applications or port the legacy applications to use the emerging ultra-fast storage technologies without being constrained by the limitations of the conventional OS storage stack.