This work introduces a GPU storage expansion solution utilizing CXL, featuring a novel GPU system design with multiple CXL root ports for integrating diverse storage media (DRAMs and/or SSDs). We developed and siliconized a custom CXL controller integrated at the hardware RTL level, achieving two-digit nanosecond roundtrip latency, the first in the field. This study also includes speculative read and deterministic store mechanisms to efficiently manage read and write operations to hide the endpoint's backend media latency variation. Performance evaluations reveal our approach significantly outperforms existing methods, marking a substantial advancement in GPU storage technology.

Processing data in storage is an energy-efficient solution to examine massive datasets. However, a general incarnation of such well-known task-offloading model in a real system is unfortunately unsuccessful due to not only poor performance but also many practical challenges, such as limited processing capabilities and high vulnerabilities at the storage-level. We propose DockerSSD, a fully flexible in-storage processing (ISP) model that can run a variety of applications near flash without their source-level modification. Specifically, it enables lightweight OS-level virtualization in modern SSDs, which allows the storage intelligence to be well harmonized with existing computing environment and makes ISP even faster. Instead of developing a vendor-specific ISP to offload, DockerSSD can reuse existing Docker images, create containers as a self-governing execution object in storage, and process data directly where they are in real-time. To this end, we design a new communication method and virtual firmware that operate together to download Docker images and manage their container execution without a change of the existing storage interface and runtime. We further accelerate ISP and reduce the execution latency by automating container-related network and I/O handling data paths over hardware. Our evaluation shows that DockerSSD is 2.0 × faster than state-of-the-art ISP models for workloads with a high volume of system calls or file accesses. Moreover, it demonstrates a reduction in power and energy consumption by 1.6 × and 2.3 × respectively.

We propose CXL-ANNS , a software-hardware collaborative approach to enable scalable approximate nearest neighbor search (ANNS) services. To this end, we first disaggregate DRAM from the host via compute express link (CXL) and place all essential datasets into its memory pool. While this CXL memory pool allows ANNS to handle billion-point graphs without an accuracy loss, we observe that the search performance significantly degrades because of CXL’s far-memory-like characteristics. To address this, CXL-ANNS considers the node-level relationship and caches the neighbors in local memory, which are expected to visit most frequently. For the uncached nodes, CXL-ANNS prefetches a set of nodes most likely to visit soon by understanding the graph traversing behaviors of ANNS. CXL-ANNS is also aware of the architectural structures of the CXL interconnect network and lets different hardware components collaborate with each other for the search. Furthermore, it relaxes the execution dependency of neighbor search tasks and allows ANNS to utilize all hardware in the CXL network in parallel. Our evaluation shows that CXL-ANNS exhibits 93.3% lower query latency than state-of-the-art ANNS platforms that we tested. CXL-ANNS also outperforms an oracle ANNS system that has unlimited local DRAM capacity by 68.0%, in terms of latency.

We present GraphTensor, a comprehensive open-source framework that supports efficient parallel neural network processing on large graphs. GraphTensor offers a set of easy-to-use programming primitives that appreciate both graph and neural network execution behaviors from the beginning (graph sampling) to the end (dense data processing). Our framework runs diverse graph neural network (GNN) models in a destination-centric, feature-wise manner, which can significantly shorten training execution times in a GPU. In addition, GraphTensor rearranges multiple GNN kernels based on their system hyperparameters in a self-governing manner, thereby reducing the processing dimensionality and the latencies further. From the end-to-end execution viewpoint, GraphTensor significantly shortens the service-level GNN latency by applying pipeline parallelism for efficient graph dataset preprocessing. Our evaluation shows that GraphTensor exhibits 1.4× better training performance than emerging GNN frameworks under the execution of large-scale, real-world graph workloads. For the end-to-end services, GraphTensor reduces training latencies of an advanced version of the GNN frameworks (optimized for multi-threaded graph sampling) by 2.4×, on average.

This paper discusses the main benefits of ZNS and shows why ZNS can be deprived of internal parallelism when downsizing its zone writable capacity. To this end, we use two production ZNS SSDs and quantitively analyze the performance degradation caused by inter-zone interference. We then suggest a simple mechanism to detect zone-to-zone relationships generating the interference and schedule I/O requests by being aware of internal parallelism. Our evaluation results using real production ZNS devices show that our mechanism can improve the bandwidth and latency of Linux's multi-queue I/O scheduler by 1.98 × and 2.2 ×, respectively.

We investigate runtime environment characteristics and explore the challenges of conventional in-memory graph processing. This system-level analysis includes empirical results and observations, which are opposite to the existing expectations of graph application users. Specifically, since raw graph data are not the same as the in-memory graph data, processing a billion-scale graph exhausts all system resources and makes the target system unavailable due to out-of-memory at runtime.To address a lack of memory space problem for big-scale graph analysis, we configure real persistent memory devices (PMEMs) with different operation modes and system software frameworks. In this work, we introduce PMEM to a representative in-memory graph system, Ligra, and perform an in-depth analysis uncovering the performance behaviors of different PMEM-applied in-memory graph systems. Based on our observations, we modify Ligra to improve the graph processing performance with a solid level of data persistence. Our evaluation results reveal that Ligra, with our simple modification, exhibits 4.41× and 3.01× better performance than the original Ligra running on a virtual memory expansion and conventional persistent memory, respectively.