Left Ventricular noncompaction (LVNC) is a recently classified form of cardiomyopathy. Although various methods have been proposed for accurately quantifying trabeculae in the left ventricle (LV), consensus on the optimal approach remains elusive. Previous research introduced DL‐LVTQ, a deep learning solution for trabecular quantification based on a UNet 2D convolutional neural network (CNN) architecture and a graphical user interface (GUI) to streamline its use in clinical workflows. Building on this foundation, this work presents LVNC detector, an enhanced application designed to support cardiologists in the automated diagnosis of LVNC. The application integrates two segmentation models: DL‐LVTQ and ViTUNet, the latter inspired by modern hybrid architectures combining convolutional neural networks (CNNs) and transformer‐based designs. These models, implemented within an ensemble framework, leverage advancements in deep learning to improve the accuracy and robustness of magnetic resonance imaging (MRI) segmentation. Key innovations include multithreading to optimize model loading times and ensemble methods to enhance segmentation consistency across MRI slices. Additionally, the platform‐independent design ensures compatibility with Windows and Linux, eliminating complex setup requirements. The LVNC detector delivers an efficient and user‐friendly solution for LVNC diagnosis. It enables real‐time performance and allows cardiologists to select and compare segmentation models for improved diagnostic outcomes. This work demonstrates how state‐of‐the‐art machine learning techniques can seamlessly integrate into clinical practice to reduce human error and expedite diagnostic processes.

Key-value (KV) stores based on LSM tree have become a foundational layer in the storage stack of datacenters and cloud services. Current approaches for achieving reliability and availability favor reducing network traffic and send to replicas only new KV pairs. As a result, they perform costly compactions to reorganize data in both the primary and backup nodes, which increases device I/O traffic and CPU overhead, and eventually hurts overall system performance. In this paper we describe Tebis, an efficient LSM-based KV store that reduces I/O amplification and CPU overhead for maintaining the replica index. We use a primary-backup replication scheme that performs compactions only on the primary nodes and sends pre-built indexes to backup nodes, avoiding all compactions in backup nodes. Our approach includes an efficient mechanism to deal with pointer translation across nodes in the pre-built region index. Our results show that Tebis reduces pressure on backup nodes compared to performing full compactions: Throughput is increased by 1.1 -- 1.48×, CPU efficiency is increased by 1.06 -- 1.54×, and I/O amplification is reduced by 1.13 -- 1.81×, without increasing server to server network traffic excessively (by up to 1.09 -- 1.82×).

Persistent key-value stores have emerged as a main component in the data access path of modern data processing systems. However, they exhibit high CPU and I/O overhead. Nowadays, due to power limitations, it is important to reduce CPU overheads for data processing. In this article, we propose Kreon , a key-value store that targets servers with flash-based storage, where CPU overhead and I/O amplification are more significant bottlenecks compared to I/O randomness. We first observe that two significant sources of overhead in key-value stores are: (a) The use of compaction in Log-Structured Merge-Trees (LSM-Tree) that constantly perform merging and sorting of large data segments and (b) the use of an I/O cache to access devices, which incurs overhead even for data that reside in memory. To avoid these, Kreon performs data movement from level to level by using partial reorganization instead of full data reorganization via the use of a full index per-level. Kreon uses memory-mapped I/O via a custom kernel path to avoid a user-space cache. For a large dataset, Kreon reduces CPU cycles/op by up to 5.8×, reduces I/O amplification for inserts by up to 4.61×, and increases insert ops/s by up to 5.3×, compared to RocksDB.

Summary OSD+s are enhanced object‐based storage devices (OSDs) able to deal with both data and metadata operations via data and directory objects, respectively. So far, we have focused on designing and implementing efficient directory objects in OSD+s. This paper, however, presents our work on also supporting data objects and describes how the coexistence of both kinds of objects in each OSD+ is profited to efficiently implement data objects and to speed up some common file operations. We compare our OSD+‐based Fusion Parallel File System (FPFS) with Lustre and OrangeFS through different microbenchmarks and HPCS‐IO scenarios. Results show that FPFS provides a throughput up to 37× better than Lustre and up to 95× better than OrangeFS for metadata workloads. FPFS also provides 34% more bandwidth than OrangeFS for data workloads and competes with Lustre in data writes. Results also show serious scalability problems in Lustre and OrangeFS that limit their performance.

Persistent key-value stores have emerged as a main component in the data access path of modern data processing systems. However, they exhibit high CPU and I/O overhead. Today, due to power limitations it is important to reduce CPU overheads for data processing.

This paper presents two mechanisms that can significantly improve the I/O performance of both hard and solid-state drives for read operations: KDSim and REDCAP. KDSim is an in-kernel disk simulator that provides a framework for simultaneously simulating the performance obtained by different I/O system mechanisms and algorithms, and for dynamically turning them on and off, or selecting between different options or policies, to improve the overall system performance. REDCAP is a RAM-based disk cache that effectively enlarges the built-in cache present in disk drives. By using KDSim, this cache is dynamically activated/deactivated according to the throughput achieved. Results show that, by using KDSim and REDCAP together, a system can improve its I/O performance up to 88% for workloads with some spatial locality on both hard and solid-state drives, while it achieves the same performance as a ‘regular system’ for workloads with random or sequential access patterns.

Small I/O requests are important for a large number of modern workloads in the data center. Traditionally, storage systems have been able to achieve low I/O rates for small I/O operations because of hard disk drive (HDD) limitations that are capable of about 100–150 IOPS (I/O operations per second) per spindle. Therefore, the host CPU processing capacity and network link throughput have been relatively abundant for providing these low rates. With new storage device technologies, such as NAND Flash Solid State Drives (SSDs) and non-volatile memory (NVM), it is becoming common to design storage systems that are able to support millions of small IOPS. At these rates, however, both server CPU and network protocol are emerging as the main bottlenecks for achieving large rates for small I/O requests. Most storage systems in datacenters deliver I/O operations over some network protocol. Although there has been extensive work in low-latency and high-throughput networks, such as Infiniband, Ethernet has dominated the datacenter. In this work we examine how networked storage protocols over raw Ethernet can achieve low, host CPU overhead and increase network link efficiency for small I/O requests. We first analyze in detail the latency and overhead of a networked storage protocol directly over Ethernet and we point out the main inefficiencies. Then, we examine how storage protocols can take advantage of context switch elimination and adaptive batching to reduce CPU and network overhead. Our results show that raw Ethernet is appropriate for supporting fast storage systems. For 4kB requests we reduce server CPU overhead by up to 45%, we improve link utilization by up to 56%, achieving more than 88% of the theoretical link throughput. Effectively, our techniques serve 56% more I/O operations over a 10Gbits/s link than a baseline protocol that does not include our optimizations at the same CPU utilization. Overall, to the best of our knowledge, this is the first work to present a system that is able to achieve 14μs host CPU overhead on both initiator and target for small networked I/Os over raw Ethernet without hardware support. In addition, our approach is able to achieve 287K 4kB IOPS out of the 315K IOPS that are theoretically possible over a 1.2GBytes/s link.

Current technology trends for efficient use of infrastructures dictate that storage converges with computation by placing storage devices, such as NVM-based cards and drives, in the servers themselves. With converged storage the role of the interconnect among servers becomes more important for achieving high I/O throughput. Given that Ethernet is emerging as the dominant technology for datacenters, it becomes imperative to examine how to reduce protocol overheads for accessing remote storage over Ethernet interconnects. In this paper we propose Tyche, a network storage protocol directly on top of Ethernet, which does not require any hardware support from the network interface. Therefore, Tyche can be deployed in existing infrastructures and to co-exist with other Ethernet-based protocols. Tyche presents remote storage as a local block device and can support any existing filesystem. At the heart of our approach, there are two main axis: reduction of host-level overheads and scaling with the number of cores and network interfaces in a server. Both target at achieving high I/O throughput in future servers. We reduce overheads via a copy-reduction technique, storage-specific packet processing, pre-allocation of memory, and using RDMA-like operations without requiring hardware support. We transparently handle multiple NICs and offer improved scaling with the number of links and cores via reduced synchronization, proper packet queue design, and NUMA affinity management. Our results show that Tyche achieves scalable I/O throughput, up to 6.4 GB/s for reads and 6.8 GB/s for writes with 6 × 10 GigE NICs. Our analysis shows that although multiple aspects of the protocol play a role for performance, NUMA affinity is particularly important. When comparing to NBD, Tyche performs better by up to one order of magnitude.

Management of directories with millions of files, accessed by thousands of clients at the same time, is a problem recently identified in HPC environments. This paper introduces an OSD+-based technique to deal with those directories. We use directory objects in OSD+ devices for dynamically distributing a huge directory among several servers. Directory objects work independently, achieving good performance and scalability. Experiments show that, by using just 8 OSD+s and Ext4, FPFS is able to create, stat and delete more than 70,000, 120,000 and 37,000 files per second, respectively. With ReiserFS, these numbers are 118,000, 97,000 and 67,000. Experiments, however, have produced unforeseen results too. While distribution is beneficial when a huge directory is accessed by many clients, it can also downgrade the performance when several huge directories are concurrently accessed by a few clients.

We present the design and implementation of both an enhanced type of OSD device, the OSD+ device, and a metadata cluster based on it. OSD+s support data objects and directory objects. A directory object stores file names and attributes, and supports metadata--related operations. OSD+s profit the directory implementation and features of the underlying file systems used by the storage nodes, achieving a great flexibility, simplicity and small overhead. By using OSD+ devices, we show how a metadata cluster can effectively be managed by all the servers in a system, improving the performance, scalability and availability of the metadata service. The performance of our new metadata cluster has been evaluated and compared with Lustre's. The results show that our proposal obtains a better throughput than Lustre when both use a single metadata server, easily getting improvements of more than 60--80\%, and that the performance scales with the number of OSD+s.

We present a framework for simulating the performance obtained by different I/O system mechanisms and algorithms at the same time, and for dynamically turning them on and off to improve the overall system performance. A key element of this framework is the the design and implementation of a virtual disk inside the Linux kernel. Our virtual disk creates a virtual block device which is able to simulate any hard drive with a negligible overhead, without interfering with regular I/O requests. We describe the potential of our proposal in REDCAP, a RAM-based disk cache which is dynamically activated/deactivated according to the throughput achieved. The results show that, by using our virtual disk, REDCAP obtains its maximum possible improvements: up to 80% for workloads with some spatial locality, and the same performance as a ''normal system" for workloads with random or large sequential reads.