We are a newly-founded micromobility startup on a mission to improve European city life

Dott

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The third ever annual Noonies have concluded! For 3 months, netizens have cast over 105k votes for 4,961 nominees across 586 awards from 6 different categories! Now that (congrats!), we would like to share more about the prizes that they all win, and a bit more about HackerNoon’s stance on web3, privacy and the internet! This series is sponsored by our partners and . all winners have been announced Get.Tech Avast have been old friends of HackerNoon. They’ve not just sponsored the Noonies events (hosted on ), but are also giving away free domains for all winners. Dottech Domains noonies.tech If you’re a first prize winner, you’ll get a free domain for 5 years, second-place winners get a 3-year registration, and the third-place winners get a 1-year registration. What even is a domain? You know that computers only read 0s and 1s. Right? ** ** But humans read letters and words too!** ** Thus, while you might type in twitter.com or hackernoon.com, your browser converts that text into what is called an IP address. The IP address is the real location where the website is hosted and help make the task of seamless conversion of something like a hackernoon.com typed by you to an IP address like 104.21.54.123. Domain Registries In hackernoon.com, the hackernoon part is called a domain name and the .com is called the TLD (Top-Level Domain). Just as there are several domain names, there are several TLDs. For example, in noonies.tech, noonies is the domain name and .tech is the TLD. As a Noonies winner, depending on whether you came in 1st, 2nd, or 3rd, you’ll get a coupon code to redeem any non-premium .tech domain name for FREE. ** **

How to Claim Your Free Dottech Domain: For Noonies Winners with Love

, the leading new domain extension for the tech ecosystem, has launched , a pitch competition in partnership with , the world’s largest community of startups, founders, innovators, and creators, for idea-stage entrepreneurs to win $10,000 in equity-free funding and over $100,000 in startup benefits. .Tech Domains Pitch.Tech Startup Grind The virtual competition comes as innovation and digitization fueled by the COVID-19 crisis continues to surge across industries and power a startup boom, causing global VC funding to reach an with $125B invested. With just over 3% of that global funding going to seed stage entrepreneurs, .Tech Domains and Startup Grind are coming together to provide a platform that empowers idea-stage entrepreneurs at the start of their journey while minimizing the common barriers and hurdles that are often present during the early stages of building a startup. all-time high in the first quarter of 2021 Following approval, applicants will be assigned a unique pitch.tech url to promote their ideas and receive votes. The top ideas will participate in cohort finals where Startup Grind chapter directors from Phoenix, Miami, Boston, Hollywood/West LA and New Haven will choose which finalists will proceed to the Grand Finals. The ultimate prize winner of Pitch.Tech will be chosen at the grand finale where finalists will participate in a virtual pitch battle presided over by a jury of industry experts and investors who will evaluate the ideas, validate the business models, and choose the winner based on various aspects such as the entrepreneur’s creativity, confidence and impact. “We are proud to partner with Startup Grind on Pitch.Tech to support idea-stage entrepreneurs in bringing their innovations to life,” said Suman Das, Sr. Director of Brand Operations, .Tech Domains. “This virtual competition provides an opportunity for idea-stage entrepreneurs from all backgrounds to kickstart their startup journey and get access to resources and visibility that they need in the launch-phase.” “Startup Grind’s mission has always been to help founders, entrepreneurs, and startups succeed, regardless of geographic location or socioeconomic circumstance,” said Derek Andersen, Co-founder & CEO, Startup Grind. “We are excited to partner with .Tech Domains to further our work supporting the startup community. Pitch.Tech is the perfect extension of our mission and presents a rare opportunity to support idea-stage entrepreneurs with the resources they need to take the first step towards launch.” For more information on the competition and to apply, please visit: . www.pitch.tech

[ANN] Pitch.Tech - A Dottech Domains & Startup Grind Competition For Ideapreneurs

How We Monitor Jupiter’s Fiery Moon Without Leaving Earth

What’s Next for Jupiter’s Fiery Moon, Io?

Jupiter’s Plasma Torus Throws a Tantrum—Blame It on Io?

Io’s Mass Supply to Jupiter’s Magnetosphere: What We Know and What We Don’t

How Jupiter’s Fiery Moon Feeds the Jovian Magnetosphere

Jupiter’s Moon Io Keeps Leaking, and Scientists Are Watching

How Jupiter’s Plasma Torus Strips Away Io’s Atmosphere

The Leaky Moon: Why Io’s Atmosphere is Always Trying to Escape

The Curious Case of Io’s Breathing Skies

Io: The Volcanic Moon That Won’t Stop Spewing

The Stability and Variability of Jupiter I's Plasma Torus

How Io, a Tiny Moon, Keeps Jupiter’s Space Weather on Its Toes

As an Upland player, I’ve experienced everything from treasure hunts to neighborhood competitions, but $SPARKLET Warz takes Upland gameplay to a whole new level. Here’s my honest review of the first-ever large-scale faction-based challenge. While it introduces some seriously exciting mechanics, there are a few clunky elements and points of confusion that might make it challenging for new or even seasoned players. Factions and Scoring The concept of $SPARKLET Warz is fresh and ambitious. The introduction of factions — Krypto Knights, Purge Pirates, and Cypher Cyborgs — adds a role-playing dimension that Upland hasn’t fully explored before. Each faction has its own unique identity, and I love how it isn’t just cosmetic; your faction choice impacts your strategy and experience in the game. The scoring mechanics sound engaging. The progressive scoring system, where daily contributions build up over time, rewards consistent gameplay rather than one-time effort. This motivates players to stay engaged throughout the week, making each day feel like a new challenge. Faction Superstructures The introduction of faction superstructures is one of my favorite parts. Requiring a whopping 40,000 Spark hours to build, these massive projects demand serious teamwork and collaboration. They’re not just point-earning behemoths — they’re a visual testament to your faction’s dominance. It’s thrilling to think of Tokyo dotted with these monolithic structures representing a faction's hard-earned effort. Offensive and Defensive Gameplay Finally, the inclusion of offensive and defensive gameplay through Cannons, Chests, and the ultimate weapon, the Kart, brings a competitive edge that Upland’s community-based gameplay was missing. It adds a layer of strategy that’ll make this event stand out from more passive events like treasure hunts or ornament decorating.' The Good The faction pass burning mechanic is a bit confusing at first glance. The explanation that “the first pass you burn locks in your faction” seems straightforward, but the process of trading passes to secure your desired faction could get messy. What if someone burns a pass accidentally or trades incorrectly? It’s not entirely clear if there’s a way to fix that. Another potential pain point is the randomization of faction passes. While trading is possible, the need to rely on others to secure your preferred faction feels like an unnecessary hurdle. If I have a clear allegiance in mind, I’d rather just pick my faction outright instead of dealing with random draws and potential trade logistics. Challenges and Hurdles The bundle mechanics are another area that might create friction. Bundles are only available after burning a faction pass, and you can only purchase bundles for your specific faction. It’s a neat concept to lock players into their faction’s ecosystem, but for casual players, it might feel restrictive and confusing if they’re not sure what bundles they need or if they miss the chance to grab one. One big question is around the strategic placement of map assets. While the blog mentions offensive and defensive items (Cannons and Chests), it’s not immediately clear how the actual placement process works. Does it happen in real-time, or can you plan your moves in advance? Also, the radii of these assets are said to be dynamic and color-coded, but it’s hard to picture how intuitive this system will feel in the middle of a battle. Concerns About the Kart Another area of confusion is the Kart, the ultimate weapon. Its description as a “devastating tool of chaos” sounds incredible, but with its ability to wipe out entire areas of points, it could easily disrupt faction balance. Will there be limits to how many Karts can be used, or is it just a free-for-all? It’s unclear how this fits into the fairness of gameplay. Scoring Progression The scoring progression is a bit intimidating for new players. The daily percentage increases and progressive score mechanics are cool, but they feel like they’re aimed at more hardcore players who can check in every day. For casual players, it might feel overwhelming to keep up with. Lastly, there’s a question of whether smaller factions will feel outmatched. If one faction attracts more experienced players or better strategists, will the other two feel like they’re just cannon fodder? Final Thoughts $SPARKLET Warz has the potential to be a game-changer for Upland. The event's focus on teamwork, strategy, and faction-based gameplay is a refreshing change that could pull players deeper into the Upland experience. However, some mechanics feel clunky, and there are areas where better clarity or simplification could make the event more accessible. For hardcore players, this event is a dream — full of strategy, competition, and the opportunity to work with teammates to claim dominance. For more casual players, though, the complexity and commitment required might feel overwhelming. I’m excited to jump into $SPARKLET Warz and see how it all plays out, but I hope Upland takes player feedback seriously during this beta run to make future iterations even more streamlined and inclusive. For now, I’m proudly aligning with the Cypher Cyborgs — bring on the battle for Tokyo! Score★★★★☆ (4/5 Stars) An exciting, ambitious event with a few bumps to iron out. As an Upland player, I’ve experienced everything from treasure hunts to neighborhood competitions, but $SPARKLET Warz takes Upland gameplay to a whole new level. Here’s my honest review of the first-ever large-scale faction-based challenge. While it introduces some seriously exciting mechanics, there are a few clunky elements and points of confusion that might make it challenging for new or even seasoned players. Factions and Scoring Factions and Scoring The concept of $SPARKLET Warz is fresh and ambitious. The introduction of factions — Krypto Knights, Purge Pirates, and Cypher Cyborgs — adds a role-playing dimension that Upland hasn’t fully explored before. Each faction has its own unique identity, and I love how it isn’t just cosmetic; your faction choice impacts your strategy and experience in the game. The scoring mechanics sound engaging. The progressive scoring system, where daily contributions build up over time, rewards consistent gameplay rather than one-time effort. This motivates players to stay engaged throughout the week, making each day feel like a new challenge. Faction Superstructures Faction Superstructures The introduction of faction superstructures is one of my favorite parts. Requiring a whopping 40,000 Spark hours to build, these massive projects demand serious teamwork and collaboration. They’re not just point-earning behemoths — they’re a visual testament to your faction’s dominance. It’s thrilling to think of Tokyo dotted with these monolithic structures representing a faction's hard-earned effort. Offensive and Defensive Gameplay Offensive and Defensive Gameplay Finally, the inclusion of offensive and defensive gameplay through Cannons, Chests, and the ultimate weapon, the Kart, brings a competitive edge that Upland’s community-based gameplay was missing. It adds a layer of strategy that’ll make this event stand out from more passive events like treasure hunts or ornament decorating.' The Good The Good The faction pass burning mechanic is a bit confusing at first glance. The explanation that “the first pass you burn locks in your faction” seems straightforward, but the process of trading passes to secure your desired faction could get messy. What if someone burns a pass accidentally or trades incorrectly? It’s not entirely clear if there’s a way to fix that. Another potential pain point is the randomization of faction passes. While trading is possible, the need to rely on others to secure your preferred faction feels like an unnecessary hurdle. If I have a clear allegiance in mind, I’d rather just pick my faction outright instead of dealing with random draws and potential trade logistics. Challenges and Hurdles Challenges and Hurdles The bundle mechanics are another area that might create friction. Bundles are only available after burning a faction pass, and you can only purchase bundles for your specific faction. It’s a neat concept to lock players into their faction’s ecosystem, but for casual players, it might feel restrictive and confusing if they’re not sure what bundles they need or if they miss the chance to grab one. One big question is around the strategic placement of map assets. While the blog mentions offensive and defensive items (Cannons and Chests), it’s not immediately clear how the actual placement process works. Does it happen in real-time, or can you plan your moves in advance? Also, the radii of these assets are said to be dynamic and color-coded, but it’s hard to picture how intuitive this system will feel in the middle of a battle. Concerns About the Kart Concerns About the Kart Another area of confusion is the Kart, the ultimate weapon. Its description as a “devastating tool of chaos” sounds incredible, but with its ability to wipe out entire areas of points, it could easily disrupt faction balance. Will there be limits to how many Karts can be used, or is it just a free-for-all? It’s unclear how this fits into the fairness of gameplay. Scoring Progression Scoring Progression The scoring progression is a bit intimidating for new players. The daily percentage increases and progressive score mechanics are cool, but they feel like they’re aimed at more hardcore players who can check in every day. For casual players, it might feel overwhelming to keep up with. Lastly, there’s a question of whether smaller factions will feel outmatched. If one faction attracts more experienced players or better strategists, will the other two feel like they’re just cannon fodder? Final Thoughts Final Thoughts $SPARKLET Warz has the potential to be a game-changer for Upland. The event's focus on teamwork, strategy, and faction-based gameplay is a refreshing change that could pull players deeper into the Upland experience. However, some mechanics feel clunky, and there are areas where better clarity or simplification could make the event more accessible. For hardcore players, this event is a dream — full of strategy, competition, and the opportunity to work with teammates to claim dominance. For more casual players, though, the complexity and commitment required might feel overwhelming. I’m excited to jump into $SPARKLET Warz and see how it all plays out, but I hope Upland takes player feedback seriously during this beta run to make future iterations even more streamlined and inclusive. For now, I’m proudly aligning with the Cypher Cyborgs — bring on the battle for Tokyo! Score ★★★★☆ (4/5 Stars) An exciting, ambitious event with a few bumps to iron out.

Upland's $SPARKLET Warz Takes Metaverse Gameplay to a Whole New Level

Smart Hotels Are Promising to Fix Travelers' Woes

স্মার্ট হোটেলগুলি ভ্রমণকারীদের সমস্যার সমাধান করার প্রতিশ্রুতি দিচ্ছে৷

Els hotels intel·ligents prometen solucionar els problemes dels viatgers

Los hoteles inteligentes prometen solucionar los problemas de los viajeros

Az intelligens szállodák azt ígérik, hogy orvosolják az utazók gondjait

Gli hotel intelligenti promettono di risolvere i problemi dei viaggiatori

スマートホテルは旅行者の悩みを解決すると約束している

스마트 호텔, 여행객의 고민을 해결해 주겠다고 약속

Hotel Pintar Berjanji untuk Memperbaiki Kesengsaraan Pengembara

स्मार्ट होटलहरूले यात्रुहरूको समस्या समाधान गर्ने प्रतिज्ञा गर्दैछन्

Smart Hotels Ti Tshembisa Ku Lulamisa Makhombo Ya Vafambi

Khách sạn thông minh hứa hẹn sẽ giải quyết nỗi lo của du khách

智能酒店有望解决旅行者的烦恼

Table of Links Abstract and 1 Introduction 2 Background and 2.1 Transformer-Based Large Language Models 2.2 LLM Service & Autoregressive Generation 2.3 Batching Techniques for LLMs 3 Memory Challenges in LLM Serving 3.1 Memory Management in Existing Systems 4 Method and 4.1 PagedAttention 4.2 KV Cache Manager 4.3 Decoding with PagedAttention and vLLM 4.4 Application to Other Decoding Scenarios 4.5 Scheduling and Preemption 4.6 Distributed Execution 5 Implementation 6 Evaluation and 6.1 Experimental Setup 6.2 Basic Sampling 6.3 Parallel Sampling and Beam Search 6.4 Shared prefix 6.5 Chatbot 7 Ablation Studies 8 Discussion 9 Related Work 10 Conclusion, Acknowledgement and References 4.4 Application to Other Decoding Scenarios §4.3 shows how PagedAttention and vLLM handle basic decoding algorithms, such as greedy decoding and sampling, that take one user prompt as input and generate a single output sequence. In many successful LLM applications [18, 34], an LLM service must offer more complex decoding scenarios that exhibit complex accessing patterns and more opportunities for memory sharing. We show the general applicability of vLLM on them in this section. Parallel sampling. In LLM-based program assistants [6, 18], an LLM generates multiple sampled outputs for a single input prompt; users can choose a favorite output from various candidates. So far we have implicitly assumed that a request generates a single sequence. In the remainder of this paper, we assume the more general case in which a request generates multiple sequences. In parallel sampling, one request includes multiple samples sharing the same input prompt, allowing the KV cache of the prompt to be shared as well. Via its PagedAttention and paged memory management, vLLM can realize this sharing easily and save memory. Fig. 8 shows an example of parallel decoding for two outputs. Since both outputs share the same prompt, we only reserve space for one copy of the prompt’s state at the prompt phase; the logical blocks for the prompts of both sequences are mapped to the same physical blocks: the logical block 0 and 1 of both sequences are mapped to physical blocks 7 and 1, respectively. Since a single physical block can be mapped to multiple logical blocks, we introduce a reference count for each physical block. In this case, the reference counts for physical blocks 7 and 1 are both 2. At the generation phase, the two outputs sample different output tokens and need separate storage for KV cache. vLLM implements a copy-onwrite mechanism at the block granularity for the physical blocks that need modification by multiple sequences, similar to the copy-on-write technique in OS virtual memory (e.g., when forking a process). Specifically, in Fig. 8, when sample A1 needs to write to its last logical block (logical block 1), vLLM recognizes that the reference count of the corresponding physical block (physical block 1) is greater than 1; it allocates a new physical block (physical block 3), instructs the block engine to copy the information from physical block 1, and decreases the reference count to 1. Next, when sample A2 writes to physical block 1, the reference count is already reduced to 1; thus A2 directly writes its newly generated KV cache to physical block 1. In summary, vLLM enables the sharing of most of the space used to store the prompts’ KV cache across multiple output samples, with the exception of the final logical block, which is managed by a copy-on-write mechanism. By sharing physical blocks across multiple samples, memory usage can be greatly reduced, especially for long input prompts. Beam search. In LLM tasks like machine translation [59], the users expect the top-𝑘 most appropriate translations output by the LLM. Beam search [49] is widely used to decode the most probable output sequence from an LLM, as it mitigates the computational complexity of fully traversing the sample space. The algorithm relies on the beam width parameter 𝑘, which determines the number of top candidates retained at every step. During decoding, beam search expands each candidate sequence in the beam by considering all possible tokens, computes their respective probabilities using the LLM, and retains the top-𝑘 most probable sequences out of 𝑘 · |𝑉 | candidates, where |𝑉 | is the vocabulary size. Unlike parallel decoding, beam search facilities sharing not only the initial prompt blocks but also other blocks across different candidates, and the sharing patterns dynamically change as the decoding process advances, similar to the process tree in the OS created by compound forks. Fig. 9 shows how vLLM manages the KV blocks for a beam search example with 𝑘 = 4. Prior to the iteration illustrated as the dotted line, each candidate sequence has used 4 full logical blocks. All beam candidates share the first block 0 (i.e., prompt). Candidate 3 digresses from others from the second block. Candidates 0-2 share the first 3 blocks and diverge at the fourth block. At subsequent iterations, the top-4 probable candidates all originate from candidates 1 and 2. As the original candidates 0 and 3 are no longer among the top candidates, their logical blocks are freed, and the reference counts of corresponding physical blocks are reduced. vLLM frees all physical blocks whose reference counts reach 0 (blocks 2, 4, 5, 8). Then, vLLM allocates new physical blocks (blocks 9-12) to store the new KV cache from the new candidates. Now, all candidates share blocks 0, 1, 3; candidates 0 and 1 share block 6, and candidates 2 and 3 further share block 7. Previous LLM serving systems require frequent memory copies of the KV cache across the beam candidates. For example, in the case shown in Fig. 9, after the dotted line, candidate 3 would need to copy a large portion of candidate 2’s KV cache to continue generation. This frequent memory copy overhead is significantly reduced by vLLM’s physical block sharing. In vLLM, most blocks of different beam candidates can be shared. The copy-on-write mechanism is applied only when the newly generated tokens are within an old shared block, as in parallel decoding. This involves only copying one block of data. Shared prefix. Commonly, the LLM user provides a (long) description of the task including instructions and example inputs and outputs, also known as system prompt [36]. The description is concatenated with the actual task input to form the prompt of the request. The LLM generates outputs based on the full prompt. Fig. 10 shows an example. Moreover, the shared prefix can be further tuned, via prompt engineering, to improve the accuracy of the downstream tasks [26, 27]. For this type of application, many user prompts share a prefix, thus the LLM service provider can store the KV cache of the prefix in advance to reduce the redundant computation spent on the prefix. In vLLM, this can be conveniently achieved by reserving a set of physical blocks for a set of predefined shared prefixes by the LLM service provider, as how OS handles shared library across processes. A user input prompt with the shared prefix can simply map its logical blocks to the cached physical blocks (with the last block marked copy-on-write). The prompt phase computation only needs to execute on the user’s task input. Mixed decoding methods. The decoding methods discussed earlier exhibit diverse memory sharing and accessing patterns. Nonetheless, vLLM facilitates the simultaneous processing of requests with different decoding preferences, which existing systems cannot efficiently do. This is because vLLM conceals the complex memory sharing between different sequences via a common mapping layer that translates logical blocks to physical blocks. The LLM and its execution kernel only see a list of physical block IDs for each sequence and do not need to handle sharing patterns across sequences. Compared to existing systems, this approach broadens the batching opportunities for requests with different sampling requirements, ultimately increasing the system’s overall throughput. This paper is available on arxiv under CC BY 4.0 DEED license. Authors:
(1) Woosuk Kwon, UC Berkeley with Equal contribution;
(2) Zhuohan Li, UC Berkeley with Equal contribution;
(3) Siyuan Zhuang, UC Berkeley;
(4) Ying Sheng, UC Berkeley and Stanford University;
(5) Lianmin Zheng, UC Berkeley;
(6) Cody Hao Yu, Independent Researcher;
(7) Cody Hao Yu, Independent Researcher;
(8) Joseph E. Gonzalez, UC Berkeley;
(9) Hao Zhang, UC San Diego;
(10) Ion Stoica, UC Berkeley. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background and 2.1 Transformer-Based Large Language Models 2 Background and 2.1 Transformer-Based Large Language Models 2.2 LLM Service & Autoregressive Generation 2.2 LLM Service & Autoregressive Generation 2.3 Batching Techniques for LLMs 2.3 Batching Techniques for LLMs 3 Memory Challenges in LLM Serving 3 Memory Challenges in LLM Serving 3.1 Memory Management in Existing Systems 3.1 Memory Management in Existing Systems 4 Method and 4.1 PagedAttention 4 Method and 4.1 PagedAttention 4.2 KV Cache Manager 4.2 KV Cache Manager 4.3 Decoding with PagedAttention and vLLM 4.3 Decoding with PagedAttention and vLLM 4.4 Application to Other Decoding Scenarios 4.4 Application to Other Decoding Scenarios 4.5 Scheduling and Preemption 4.5 Scheduling and Preemption 4.6 Distributed Execution 4.6 Distributed Execution 5 Implementation 5 Implementation 6 Evaluation and 6.1 Experimental Setup 6 Evaluation and 6.1 Experimental Setup 6.2 Basic Sampling 6.2 Basic Sampling 6.3 Parallel Sampling and Beam Search 6.3 Parallel Sampling and Beam Search 6.4 Shared prefix 6.4 Shared prefix 6.5 Chatbot 6.5 Chatbot 7 Ablation Studies 7 Ablation Studies 8 Discussion 8 Discussion 9 Related Work 9 Related Work 10 Conclusion, Acknowledgement and References 10 Conclusion, Acknowledgement and References 4.4 Application to Other Decoding Scenarios §4.3 shows how PagedAttention and vLLM handle basic decoding algorithms, such as greedy decoding and sampling, that take one user prompt as input and generate a single output sequence. In many successful LLM applications [18, 34], an LLM service must offer more complex decoding scenarios that exhibit complex accessing patterns and more opportunities for memory sharing. We show the general applicability of vLLM on them in this section. Parallel sampling. In LLM-based program assistants [6, 18], an LLM generates multiple sampled outputs for a single input prompt; users can choose a favorite output from various candidates. So far we have implicitly assumed that a request Parallel sampling. generates a single sequence. In the remainder of this paper, we assume the more general case in which a request generates multiple sequences. In parallel sampling, one request includes multiple samples sharing the same input prompt, allowing the KV cache of the prompt to be shared as well. Via its PagedAttention and paged memory management, vLLM can realize this sharing easily and save memory. Fig. 8 shows an example of parallel decoding for two outputs. Since both outputs share the same prompt, we only reserve space for one copy of the prompt’s state at the prompt phase; the logical blocks for the prompts of both sequences are mapped to the same physical blocks: the logical block 0 and 1 of both sequences are mapped to physical blocks 7 and 1, respectively. Since a single physical block can be mapped to multiple logical blocks, we introduce a reference count for each physical block. In this case, the reference counts for physical blocks 7 and 1 are both 2. At the generation phase, the two outputs sample different output tokens and need separate storage for KV cache. vLLM implements a copy-onwrite mechanism at the block granularity for the physical blocks that need modification by multiple sequences, similar to the copy-on-write technique in OS virtual memory (e.g., when forking a process). Specifically, in Fig. 8, when sample A1 needs to write to its last logical block (logical block 1), vLLM recognizes that the reference count of the corresponding physical block (physical block 1) is greater than 1; it allocates a new physical block (physical block 3), instructs the block engine to copy the information from physical block 1, and decreases the reference count to 1. Next, when sample A2 writes to physical block 1, the reference count is already reduced to 1; thus A2 directly writes its newly generated KV cache to physical block 1. In summary, vLLM enables the sharing of most of the space used to store the prompts’ KV cache across multiple output samples, with the exception of the final logical block, which is managed by a copy-on-write mechanism. By sharing physical blocks across multiple samples, memory usage can be greatly reduced, especially for long input prompts. Beam search. In LLM tasks like machine translation [59], the users expect the top-𝑘 most appropriate translations output by the LLM. Beam search [49] is widely used to decode the most probable output sequence from an LLM, as it mitigates the computational complexity of fully traversing the Beam search. sample space. The algorithm relies on the beam width parameter 𝑘, which determines the number of top candidates retained at every step. During decoding, beam search expands each candidate sequence in the beam by considering all possible tokens, computes their respective probabilities using the LLM, and retains the top-𝑘 most probable sequences out of 𝑘 · |𝑉 | candidates, where |𝑉 | is the vocabulary size. Unlike parallel decoding, beam search facilities sharing not only the initial prompt blocks but also other blocks across different candidates, and the sharing patterns dynamically change as the decoding process advances, similar to the process tree in the OS created by compound forks. Fig. 9 shows how vLLM manages the KV blocks for a beam search example with 𝑘 = 4. Prior to the iteration illustrated as the dotted line, each candidate sequence has used 4 full logical blocks. All beam candidates share the first block 0 (i.e., prompt). Candidate 3 digresses from others from the second block. Candidates 0-2 share the first 3 blocks and diverge at the fourth block. At subsequent iterations, the top-4 probable candidates all originate from candidates 1 and 2. As the original candidates 0 and 3 are no longer among the top candidates, their logical blocks are freed, and the reference counts of corresponding physical blocks are reduced. vLLM frees all physical blocks whose reference counts reach 0 (blocks 2, 4, 5, 8). Then, vLLM allocates new physical blocks (blocks 9-12) to store the new KV cache from the new candidates. Now, all candidates share blocks 0, 1, 3; candidates 0 and 1 share block 6, and candidates 2 and 3 further share block 7. Previous LLM serving systems require frequent memory copies of the KV cache across the beam candidates. For example, in the case shown in Fig. 9, after the dotted line, candidate 3 would need to copy a large portion of candidate 2’s KV cache to continue generation. This frequent memory copy overhead is significantly reduced by vLLM’s physical block sharing. In vLLM, most blocks of different beam candidates can be shared. The copy-on-write mechanism is applied only when the newly generated tokens are within an old shared block, as in parallel decoding. This involves only copying one block of data. Shared prefix. Commonly, the LLM user provides a (long) description of the task including instructions and example inputs and outputs, also known as system prompt [36]. The description is concatenated with the actual task input to form the prompt of the request. The LLM generates outputs based Shared prefix. on the full prompt. Fig. 10 shows an example. Moreover, the shared prefix can be further tuned, via prompt engineering, to improve the accuracy of the downstream tasks [26, 27]. For this type of application, many user prompts share a prefix, thus the LLM service provider can store the KV cache of the prefix in advance to reduce the redundant computation spent on the prefix. In vLLM, this can be conveniently achieved by reserving a set of physical blocks for a set of predefined shared prefixes by the LLM service provider, as how OS handles shared library across processes. A user input prompt with the shared prefix can simply map its logical blocks to the cached physical blocks (with the last block marked copy-on-write). The prompt phase computation only needs to execute on the user’s task input. Mixed decoding methods. The decoding methods discussed earlier exhibit diverse memory sharing and accessing patterns. Nonetheless, vLLM facilitates the simultaneous processing of requests with different decoding preferences, which existing systems cannot efficiently do. This is because vLLM conceals the complex memory sharing between different sequences via a common mapping layer that translates logical blocks to physical blocks. The LLM and its execution kernel only see a list of physical block IDs for each sequence and do not need to handle sharing patterns across sequences. Compared to existing systems, this approach broadens the batching opportunities for requests with different sampling requirements, ultimately increasing the system’s overall throughput. Mixed decoding methods. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors: (1) Woosuk Kwon, UC Berkeley with Equal contribution; (2) Zhuohan Li, UC Berkeley with Equal contribution; (3) Siyuan Zhuang, UC Berkeley; (4) Ying Sheng, UC Berkeley and Stanford University; (5) Lianmin Zheng, UC Berkeley; (6) Cody Hao Yu, Independent Researcher; (7) Cody Hao Yu, Independent Researcher; (8) Joseph E. Gonzalez, UC Berkeley; (9) Hao Zhang, UC San Diego; (10) Ion Stoica, UC Berkeley. Authors: Authors: (1) Woosuk Kwon, UC Berkeley with Equal contribution; (2) Zhuohan Li, UC Berkeley with Equal contribution; (3) Siyuan Zhuang, UC Berkeley; (4) Ying Sheng, UC Berkeley and Stanford University; (5) Lianmin Zheng, UC Berkeley; (6) Cody Hao Yu, Independent Researcher; (7) Cody Hao Yu, Independent Researcher; (8) Joseph E. Gonzalez, UC Berkeley; (9) Hao Zhang, UC San Diego; (10) Ion Stoica, UC Berkeley.

How vLLM Can Be Applied to Other Decoding Scenarios

Table of Links Abstract and Introduction


Domain and Task
2.1. Data sources and complexity
2.2. Task definition


Related Work
3.1. Text mining and NLP research overview
3.2. Text mining and NLP in industry use
3.3. Text mining and NLP for procurement
3.4. Conclusion from literature review


Proposed Methodology
4.1. Domain knowledge
4.2. Content extraction
4.3. Lot zoning
4.4. Lot item detection
4.5. Lot parsing
4.6. XML parsing, data joining, and risk indices development


Experiment and Demonstration
5.1. Component evaluation
5.2. System demonstration


Discussion
6.1. The ‘industry’ focus of the project
6.2. Data heterogeneity, multilingual and multi-task nature
6.3. The dilemma of algorithmic choices
6.4. The cost of training data


Conclusion, Acknowledgements, and References 2.2. Task definition 2.2. Task definition Relating to the project’s ultimate goal of enabling the dynamic creation of ‘supplier risk profiles’, we envisage a database scheme that is partially (for reasons of both proprietary information and simplicity) depicted in Figure 4 showing entities (rounded boxes), their attributes (dashed boxes) and relationships (lines). Briefly, we hope to create a record for each contract award that has one buyer and one supplier, with associated lots (one or multiple) and item information. We store certain attributes of the buyer, supplier, contract award, lots and items, such that we can run queries to fetch ‘supplier centric’ award information like the range of products (based on lot items) they have supplied, the monetary value and quantities for different kinds of products, their buyers, and covered geographical regions (buyer country). Previously we mentioned that one award XML may mention multiple contracts with multiple suppliers. In practice, we ‘flatten’ data extracted from the award XML into multiple contract award records. As already mentioned, much of the information captured by this schema is already in (semi-)structured format available from the tender and/or award XMLs. These can be easily extracted through XML parsing, although some light-weight data cleansing and linking may be needed (e.g., extracting the country from a buyer/supplier address, or matching buyer names from the tender and award XMLs). These tasks are relatively straightforward and will not be the focus of this article. Instead, the real challenge is extracting and populating the lot and item information (indicated in red dotted box) that is often missing in both the tender and award XMLs. Therefore, our tasks can be defined as it is shown in Figure 5 - given a tender XML, its associated award XML, and the associated tender attachments, we aim to: 1. Extract the structured lot and item information often missing in tender and award XMLs (filling the schema highlighted in the red dotted box in Figure 4; also the middle lane in Figure 5): a. From the tender attachments, identify the relevant documents, and the relevant content areas (i.e., tables, lists, paragraphs) that contain information about the lots (lot zoning); b. From the identified content areas, detect content elements (e.g., sentences) associated with lot items (lot item detection); c. From the detected lot items, parse the lot and item descriptions into a structured representation, identifying attributes such as the lot reference, name of the items, measurement units, etc (lot parsing). As an example, Figure 6 shows how we want to extract structured lot and item information from the example in Figure 3. 2. Extract the structured supplier, buyer, and contract award data from the tender and award XMLs (filling other parts of the schema shown in Figure 4; also the upper and bottom lanes in Figure 5). 3. Join the information extracted from 1 and 2 above to form supplier-centric contract records and populate the database. 4. Develop supplier risk indices using the populated database and a platform for exploring these indices. For the scope of this article, we will focus on 1), briefly cover 2) and 3) as they are more straightforward and completed outside the scope of this project, and skip 4). Authors:
(1) Ziqi Zhang*, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Ziqi.Zhang@sheffield.ac.uk);
(2) Tomas Jasaitis, Vamstar Ltd., London (Tomas.Jasaitis@vamstar.io);
(3) Richard Freeman, Vamstar Ltd., London (Richard.Freeman@vamstar.io);
(4) Rowida Alfrjani, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Rowida.Alfrjani@sheffield.ac.uk);
(5) Adam Funk, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Adam.Funk@sheffield.ac.uk). This paper is available on arxiv under CC BY 4.0 license. Table of Links Abstract and Introduction Domain and Task
2.1. Data sources and complexity
2.2. Task definition Related Work
3.1. Text mining and NLP research overview
3.2. Text mining and NLP in industry use
3.3. Text mining and NLP for procurement
3.4. Conclusion from literature review Proposed Methodology
4.1. Domain knowledge
4.2. Content extraction
4.3. Lot zoning
4.4. Lot item detection
4.5. Lot parsing
4.6. XML parsing, data joining, and risk indices development Experiment and Demonstration
5.1. Component evaluation
5.2. System demonstration Discussion
6.1. The ‘industry’ focus of the project
6.2. Data heterogeneity, multilingual and multi-task nature
6.3. The dilemma of algorithmic choices
6.4. The cost of training data Conclusion, Acknowledgements, and References Abstract and Introduction Abstract and Introduction Abstract and Introduction Abstract and Introduction Domain and Task 2.1. Data sources and complexity 2.2. Task definition Domain and Task Domain and Task 2.1. Data sources and complexity 2.1. Data sources and complexity 2.2. Task definition 2.2. Task definition Related Work 3.1. Text mining and NLP research overview 3.2. Text mining and NLP in industry use 3.3. Text mining and NLP for procurement 3.4. Conclusion from literature review Related Work Related Work 3.1. Text mining and NLP research overview 3.1. Text mining and NLP research overview 3.2. Text mining and NLP in industry use 3.2. Text mining and NLP in industry use 3.3. Text mining and NLP for procurement 3.3. Text mining and NLP for procurement 3.4. Conclusion from literature review 3.4. Conclusion from literature review Proposed Methodology 4.1. Domain knowledge 4.2. Content extraction 4.3. Lot zoning 4.4. Lot item detection 4.5. Lot parsing 4.6. XML parsing, data joining, and risk indices development Proposed Methodology Proposed Methodology Proposed Methodology 4.1. Domain knowledge 4.1. Domain knowledge 4.2. Content extraction 4.2. Content extraction 4.3. Lot zoning 4.3. Lot zoning 4.4. Lot item detection 4.4. Lot item detection 4.5. Lot parsing 4.5. Lot parsing 4.6. XML parsing, data joining, and risk indices development 4.6. XML parsing, data joining, and risk indices development Experiment and Demonstration 5.1. Component evaluation 5.2. System demonstration Experiment and Demonstration Experiment and Demonstration 5.1. Component evaluation 5.1. Component evaluation 5.2. System demonstration 5.2. System demonstration Discussion 6.1. The ‘industry’ focus of the project 6.2. Data heterogeneity, multilingual and multi-task nature 6.3. The dilemma of algorithmic choices 6.4. The cost of training data Discussion Discussion 6.1. The ‘industry’ focus of the project 6.1. The ‘industry’ focus of the project 6.2. Data heterogeneity, multilingual and multi-task nature 6.2. Data heterogeneity, multilingual and multi-task nature 6.3. The dilemma of algorithmic choices 6.3. The dilemma of algorithmic choices 6.4. The cost of training data 6.4. The cost of training data Conclusion, Acknowledgements, and References Conclusion, Acknowledgements, and References Conclusion, Acknowledgements, and References Conclusion, Acknowledgements, and References 2.2. Task definition 2.2. Task definition Relating to the project’s ultimate goal of enabling the dynamic creation of ‘supplier risk profiles’, we envisage a database scheme that is partially (for reasons of both proprietary information and simplicity) depicted in Figure 4 showing entities (rounded boxes), their attributes (dashed boxes) and relationships (lines). Briefly, we hope to create a record for each contract award that has one buyer and one supplier, with associated lots (one or multiple) and item information. We store certain attributes of the buyer, supplier, contract award, lots and items, such that we can run queries to fetch ‘supplier centric’ award information like the range of products (based on lot items) they have supplied, the monetary value and quantities for different kinds of products, their buyers, and covered geographical regions (buyer country). Previously we mentioned that one award XML may mention multiple contracts with multiple suppliers. In practice, we ‘flatten’ data extracted from the award XML into multiple contract award records. As already mentioned, much of the information captured by this schema is already in (semi-)structured format available from the tender and/or award XMLs. These can be easily extracted through XML parsing, although some light-weight data cleansing and linking may be needed (e.g., extracting the country from a buyer/supplier address, or matching buyer names from the tender and award XMLs). These tasks are relatively straightforward and will not be the focus of this article. Instead, the real challenge is extracting and populating the lot and item information (indicated in red dotted box) that is often missing in both the tender and award XMLs. Therefore, our tasks can be defined as it is shown in Figure 5 - given a tender XML, its associated award XML, and the associated tender attachments, we aim to: 1. Extract the structured lot and item information often missing in tender and award XMLs (filling the schema highlighted in the red dotted box in Figure 4; also the middle lane in Figure 5): a. From the tender attachments, identify the relevant documents, and the relevant content areas (i.e., tables, lists, paragraphs) that contain information about the lots (lot zoning); b. From the identified content areas, detect content elements (e.g., sentences) associated with lot items (lot item detection); c. From the detected lot items, parse the lot and item descriptions into a structured representation, identifying attributes such as the lot reference, name of the items, measurement units, etc (lot parsing). As an example, Figure 6 shows how we want to extract structured lot and item information from the example in Figure 3. 2. Extract the structured supplier, buyer, and contract award data from the tender and award XMLs (filling other parts of the schema shown in Figure 4; also the upper and bottom lanes in Figure 5). 3. Join the information extracted from 1 and 2 above to form supplier-centric contract records and populate the database. 4. Develop supplier risk indices using the populated database and a platform for exploring these indices. For the scope of this article, we will focus on 1), briefly cover 2) and 3) as they are more straightforward and completed outside the scope of this project, and skip 4). Authors: (1) Ziqi Zhang*, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Ziqi.Zhang@sheffield.ac.uk); (2) Tomas Jasaitis, Vamstar Ltd., London (Tomas.Jasaitis@vamstar.io); (3) Richard Freeman, Vamstar Ltd., London (Richard.Freeman@vamstar.io); (4) Rowida Alfrjani, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Rowida.Alfrjani@sheffield.ac.uk); (5) Adam Funk, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Adam.Funk@sheffield.ac.uk). Authors: Authors: (1) Ziqi Zhang*, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Ziqi.Zhang@sheffield.ac.uk); (2) Tomas Jasaitis, Vamstar Ltd., London (Tomas.Jasaitis@vamstar.io); (3) Richard Freeman, Vamstar Ltd., London (Richard.Freeman@vamstar.io); (4) Rowida Alfrjani, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Rowida.Alfrjani@sheffield.ac.uk); (5) Adam Funk, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Adam.Funk@sheffield.ac.uk). This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv

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