Get ready to revolutionize your ANNS research with CXL-ANNS! See you in Boston, July! We believe that CXL-ANNS will be a game-changer in the field of ANNS, and we are honored to have our work recognized by USENIX ATC. The results of our empirical evaluation are impressive, with CXL-ANNS exhibiting 22.9x lower query latency than state-of-the-art ANNS platforms and outperforming an oracle ANNS system with unlimited DRAM storage capacity by 2.9x in terms of latency. Additionally, CXL-ANNS leverages the parallel processing power of the CXL interconnect network to improve search performance even further. To address the performance degradation that can occur in CXL systems, our approach caches frequently visited data and prefetches likely data based on graph traversing behaviors of ANNS. Our paper proposes a novel approach to approximate nearest neighbor search (ANNS) that leverages the power of both software and hardware to achieve scalability and performance.We utilize compute express link (CXL) technology to separate DRAM from the host resources and place essential datasets into its memory pool, allowing us to handle billion-point graphs without sacrificing accuracy. We are thrilled to announce the acceptance of our research paper "CXL-ANNS: A Software-Hardware Collaborative Approach for Scalable Approximate Nearest Neighbor Search" at USENIX ATC, a highly-regarded conference with a rigorous review process and an acceptance rate of only 18%. We look forward to seeing the transformative impact their research will undoubtedly have on the future of memory technology. ![]() We congratulate Miryeong Kwon and Sangwon Lee on their phenomenal work and eagerly anticipate their presentation at HotStorage. They welcome fellow researchers, industry experts, and anyone with an interest in this technology, to engage with them during the event. We are eager to share their findings with the broader community at this year's HotStorage conference. This revolutionary research represents a substantial step forward in memory access and storage technology, with the potential to influence various tech industries. The enhancement reaches up to 2.8 times when compared to a CXL-SSD expanded memory pool without a CXL-prefetcher. Their evaluation results are staggering, revealing that the proposed prefetcher can significantly boost the performance of various graph applications, characterized by their highly irregular memory access patterns. Notably, this approach has been designed with CPU design area constraints in mind, underlining the real-world applicability of this research. To overcome this, we introduce an "expander-driven CXL prefetcher," a novel solution that shifts primary Last Level Cache (LLC) prefetch tasks from the host Central Processing Unit (CPU) to CXL-SSDs. However, this capability traditionally comes at a cost, mainly slower speeds compared to Dynamic Random Access Memory (DRAM). The research centers on the integration of Compute Express Link (CXL) with Solid State Drives (SSDs), a technology capable of scalable access to large memory. We present a groundbreaking solution to a significant challenge in the realm of data storage and memory access. We are thrilled to announce that a cutting-edge research paper, penned by Miryeong Kwon and Sangwon Lee, has been accepted at this year's HotStorage conference. Stay connected for more updates on our future endeavors as we continue this exhilarating expedition of innovation and discovery. We are perpetually pushing the boundaries, exploring the realms of possibility with state-of-the-art technologies like CXL. ![]() As we progress on our journey, we are enthusiastic about encountering more such opportunities to share our expertise and discoveries with the wider tech community. This sophisticated methodology has significantly elevated the training performance and notably reduced energy consumption, thus augmenting system efficiency. Moreover, this system adopts an advanced checkpointing technique to sequentially update model parameters and embeddings across various training batches. This amalgamation allows the graphics processing units to directly access the memory, thereby negating the need for software intervention. ![]() We utilized the versatility of CXL to flawlessly amalgamate persistent memory and graphics processing units into a cache-coherent domain. In the next part of our discourse, we introduced a resilient system specifically architected for managing voluminous recommendation datasets.
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