{"id":4150,"date":"2023-04-23T14:00:00","date_gmt":"2023-04-23T14:00:00","guid":{"rendered":"https:\/\/www.systor.org\/2023\/?page_id=4150"},"modified":"2024-09-25T19:03:49","modified_gmt":"2024-09-25T19:03:49","slug":"program2","status":"publish","type":"page","link":"https:\/\/www.systor.org\/2024\/program2\/","title":{"rendered":"Program"},"content":{"rendered":"

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<\/h3>\n

For questions join the ACM Systor Conference Slack workspace<\/a>,<\/strong>
\nthen use the relevant session’s channel (links under each session below).<\/strong><\/h3>\n

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Keynotes<\/h2>\n[\/vc_column][\/vc_row][vc_row tm_bgimagefixed=”” break_in_responsive=”991″ tm_zindex=”2″ full_width=”stretch_row” tm_responsive_css=”60233685|colbreak_no|||||||||colbreak_no|||||55px||25px||colbreak_no||||||||||colbreak_no|||||||||” css=”.vc_custom_1639489863294{padding-top: 111px !important;padding-bottom: 0px !important;}”][vc_column width=”1\/2″ tm_responsive_css=”31574345|colbreak_no|||||||||colbreak_no|||||15px||||colbreak_no||||||||||colbreak_no|||||||||” el_class=”tm-reset-all-left-991″]

Keynote 1<\/h4>\n

Breaking Barriers: Scalable Data Platform Architectures for the Next Era of AI<\/h2>\n<\/div>
<\/span><\/div><\/header>
\n

Shachar Fienblit (VAST Data)<\/strong><\/em><\/h3>\n

\u00a0<\/strong><\/em>Slack channel<\/a>
\n<\/strong><\/h3>\n

<\/div><\/div><\/div><\/div><\/section><\/div> [vc_single_image image=”4928″ style=”vc_box_circle_2″][\/vc_column][vc_column width=”1\/2″][vc_tta_accordion color=”grey” c_icon=”chevron” c_position=”right” active_section=”1″ collapsible_all=”true”][vc_tta_section title=”Abstract” tab_id=”1629234536160-db282339-36e7″][vc_column_text]AI is evolving beyond traditional text-based analysis, now encompassing multi-modal data such as images, video, and audio. This evolution exponentially increases the scale and scope of data available for analysis and requires a new Data Platform for exabyte-scale data. Scalability, Complexity and cost have historically been significant barriers to the widespread adoption of data and AI technologies. However, the emergence of new, more scalable and resilient architectures is rapidly dismantling these obstacles, unlocking a multitude of opportunities for simplification and innovation.<\/p>\n

In this talk I will focus on the algorithms and data structures we\u2019ve implemented in the VAST Data Platform, a unified global datastore, database, and AI computing engine. I will explore practical algorithms and data structures that break traditional trade-offs between speed, cost, and resiliency. Key topics will include similarity compression, error correction codes, and scalable data structures designed to optimize high performance and scalable file system, database queries and AI pipelines.[\/vc_column_text][\/vc_tta_section][vc_tta_section title=”Bio” tab_id=”1629234536247-1ed2440d-7944″][vc_column_text]Shachar Fienblit is the co-founder and Chief R&D Officer of VAST Data, a successful startup in the area of storage platform.<\/p>\n

Shachar has an extensive experience in the storage industry. \u00a0He served as the CTO and VP Engineering of Kaminario, working on on-premises all-flash array, and with the storage on-premises products of IBM.[\/vc_column_text][\/vc_tta_section][\/vc_tta_accordion][\/vc_column][\/vc_row][vc_row tm_bgimagefixed=”” break_in_responsive=”991″ tm_zindex=”2″ full_width=”stretch_row” tm_responsive_css=”60233685|colbreak_no|||||||||colbreak_no|||||55px||25px||colbreak_no||||||||||colbreak_no|||||||||” css=”.vc_custom_1725524357687{padding-top: 40px !important;padding-bottom: 0px !important;}”][vc_column width=”1\/2″ tm_responsive_css=”31574345|colbreak_no|||||||||colbreak_no|||||15px||||colbreak_no||||||||||colbreak_no|||||||||” el_class=”tm-reset-all-left-991″]

Keynote 2<\/h4>\n

Cheap, dirty, and effective: Reinforcement Learning for High Paced Control of Hardware Systems<\/h2>\n<\/div>
<\/span><\/div><\/header>
\n

Shie Manor (Technion)<\/strong><\/em><\/h3>\n

Slack channel<\/strong><\/a>
\n<\/strong><\/em><\/h3>\n

 <\/p><\/div><\/div><\/div><\/div><\/section><\/div> [vc_single_image image=”4927″ style=”vc_box_circle_2″][\/vc_column][vc_column width=”1\/2″][vc_tta_accordion color=”grey” c_icon=”chevron” c_position=”right” active_section=”1″ collapsible_all=”true”][vc_tta_section title=”Abstract” tab_id=”1629235035324-d13fd0d1-c9f1″][vc_column_text]There are many problems in hardware that can be succinctly described with the reinforcement learning (RL) framework. In such problems an agent needs to make a decision has potentially long-term consequences. While in general RL algorithm are too complex to implement in real-time hardware, we describe the tradeoffs needed to facilitate effective and efficient implementation of RL algorithms in hardware. I will focus on practical algorithms we have implemented at Nvidia for network congestion control, cache prefetching aggressiveness, and online configuration selection. I will then describe the challenges in building practical real world RL-based systems such as lack of stationarity, stability and safety requirements, etc.[\/vc_column_text][\/vc_tta_section][vc_tta_section title=”Bio” tab_id=”1629235035427-a14098de-d58a”][vc_column_text]Shie Mannor is the incumbent Edwards Chair in Engineering at the Electrical and Computer Engineering at the Technion. Shie has published over 80 journal papers and over 25<\/span>0 conference papers and his papers were cited over 31K times. Shie\u2019s research interests include machine learning and data science with an emphasis on reinforcement learning, planning and control, and analysis and control of large-scale systems. Shie has co-founded four companies and is currently a distinguished scientist at Nvidia.[\/vc_column_text][\/vc_tta_section][\/vc_tta_accordion][\/vc_column][\/vc_row][vc_row tm_bgimagefixed=”” break_in_responsive=”991″ tm_zindex=”2″ full_width=”stretch_row” tm_responsive_css=”60233685|colbreak_no|||||||||colbreak_no|||||55px||25px||colbreak_no||||||||||colbreak_no|||||||||” css=”.vc_custom_1725524384438{padding-top: 40px !important;padding-bottom: 0px !important;}”][vc_column width=”1\/2″ tm_responsive_css=”31574345|colbreak_no|||||||||colbreak_no|||||15px||||colbreak_no||||||||||colbreak_no|||||||||” el_class=”tm-reset-all-left-991″]

Keynote 3<\/h4>\n

Research Advances in Scaling BFT Consensus<\/h2>\n<\/div>
<\/span><\/div><\/header>
\n

Dahlia Malkhi (UC Santa Barbara and Chainlink Labs)<\/strong><\/em><\/h3>\n

<\/div><\/div><\/div><\/div><\/section><\/div> [vc_single_image image=”4924″ style=”vc_box_circle_2″][\/vc_column][vc_column width=”1\/2″][vc_tta_accordion color=”grey” c_icon=”chevron” c_position=”right” active_section=”1″ collapsible_all=”true”][vc_tta_section title=”Abstract” tab_id=”1629274246379-90f8241e-bd50″][vc_column_text]This talk gives a glimpse onto a decade of innovation which has been sparked by blockchains in scaling BFT Consensus.<\/p>\n

In a nutshell, a Consensus system allows a network of nodes to act in coordinated manner despite both benign outages and security (\u201cByzantine\u201d) threats. For over four decades, experts in the field have been looking for Consensus solutions that can perform well in practice.<\/p>\n

In 2018, HotStuff<\/a> transformed the landscape of practical BFT consensus solutions by introducing two key contributions, linearity<\/b> and streamlining<\/b>: HotStuff is the first BFT protocol that achieves linear communication complexity even when a fault occurs. Additionally, the HotStuff protocol is modular, consisting of a recurring broadcast module that can be pipelined, such that each broadcast extends the sequence of decisions by one.<\/p>\n

HotStuff was adopted by the famous Diem(Libra) project to drive global payment rails backed by a consortium-operated blockchain. In the end, Diem did not launch, but the technology behind it illuminated and fertilized the field.<\/p>\n

The talk overviews solutions enabled by HotStuff to open problems which emerged under a variety of settings, underscoring insights from the blockchain arena that led to new approaches.[\/vc_column_text][\/vc_tta_section][vc_tta_section title=”Bio” tab_id=”1629274246546-678008ee-1cbf”][vc_column_text]Dahlia Malkhi is a Professor in the Department of Computer Science of UCSB since 2024. She heads the Foundations of Financial Technology lab<\/a>. Her research over two decades spans broad aspects of reliability and security of distributed systems, recently with focus on blockchains and advances in financial technology. Her work resulted in over 200 publications as well as a strong impact on computing technology.<\/p>\n

Malkhi serves as Distinguished Scientist of Chainlink Labs<\/a>\u00a0 since 2022. From 2019 to 2022, she was the CTO at the Diem Association<\/a>, and Lead Researcher at Novi Financial<\/a>. In 2014, she co-founded VMware Research<\/a>\u00a0and became a Principal Researcher at VMware until 2019. Prior to that, Malkhi was a partner principal researcher at Microsoft Research, 2004-2014; an Associate Professor of the Hebrew University of Jerusalem, and a senior researcher at AT&T Labs.[\/vc_column_text][\/vc_tta_section][\/vc_tta_accordion][\/vc_column][\/vc_row][vc_section][vc_row tm_bgimagefixed=”” break_in_responsive=”991″ tm_zindex=”2″ full_width=”stretch_row” tm_responsive_css=”60233685|colbreak_no|||||||||colbreak_no|||||55px||25px||colbreak_no||||||||||colbreak_no|||||||||” css=”.vc_custom_1725524439677{padding-top: 40px !important;padding-bottom: 0px !important;}”][vc_column reduce_extra_padding=”991″ tm_responsive_css=”22114509|colbreak_no|||||||||colbreak_no|||||||||colbreak_no||||||||||colbreak_no|||||||||” css=”.vc_custom_1626073647852{padding-right: 100px !important;}”][vc_raw_html]JTNDYSUyMG5hbWUlM0QlMjJmdWxsLXByb2dyYW0lMjIlMjBzdHlsZSUzRCUyMnBvc2l0aW9uJTNBJTIwYWJzb2x1dGUlM0IlMjAlMjBsZWZ0JTNBJTIwMHB4JTNCJTIwJTIwdG9wJTNBJTIwLTEwMHB4JTNCJTIyJTNFJTNDJTJGYSUzRQ==[\/vc_raw_html][vc_separator css=”.vc_custom_1629274213962{margin-top: -30px !important;margin-bottom: 20px !important;}”]

Full Program<\/h2>\n[\/vc_column][\/vc_row][vc_row tm_bgimagefixed=”” break_in_responsive=”991″ tm_zindex=”2″ full_width=”stretch_row” tm_responsive_css=”60233685|colbreak_no|||||||||colbreak_no|||||55px||25px||colbreak_no||||||||||colbreak_no|||||||||” css=”.vc_custom_1725524616793{padding-top: 40px !important;padding-bottom: 0px !important;}”][vc_column reduce_extra_padding=”991″ width=”1\/2″ tm_responsive_css=”22114509|colbreak_no|||||||||colbreak_no|||||||||colbreak_no||||||||||colbreak_no|||||||||” css=”.vc_custom_1626073647852{padding-right: 100px !important;}”][vc_column_text]For questions join the ACM Systor Conference Slack workspace<\/a>,<\/strong>
\nthen use the relevant session’s channel (links under each session below).<\/strong><\/p>\n

Sessions:<\/strong><\/h3>\n

Session 1: Distributed Systems
\n<\/b><\/p>\n

Chair: Ningfang Mi (Northeastern),\u00a0\u00a0\u00a0 Slack channel<\/a>
\n<\/strong><\/p>\n

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