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Coalesce

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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 5 Implementation vLLM is an end-to-end serving system with a FastAPI [15] frontend and a GPU-based inference engine. The frontend extends the OpenAI API [34] interface, allowing users to customize sampling parameters for each request, such as the maximum sequence length and the beam width 𝑘. The vLLM engine is written in 8.5K lines of Python and 2K lines of C++/CUDA code. We develop control-related components including the scheduler and the block manager in Python while developing custom CUDA kernels for key operations such as PagedAttention. For the model executor, we implement popular LLMs such as GPT [5], OPT [62], and LLaMA [52] using PyTorch [39] and Transformers [58]. We use NCCL [32] for tensor communication across the distributed GPU workers. 5.1 Kernel-level Optimization Since PagedAttention introduces memory access patterns that are not efficiently supported by existing systems, we develop several GPU kernels for optimizing it. (1) Fused reshape and block write. In every Transformer layer, the new KV cache are split into blocks, reshaped to a memory layout optimized for block read, then saved at positions specified by the block table. To minimize kernel launch overheads, we fuse them into a single kernel. (2) Fusing block read and attention. We adapt the attention kernel in FasterTransformer [31] to read KV cache according to the block table and perform attention operations on the fly. To ensure coalesced memory access, we assign a GPU warp to read each block. Moreover, we add support for variable sequence lengths within a request batch. (3) Fused block copy. Block copy operations, issued by the copy-on-write mechanism, may operate on discontinuous blocks. This can lead to numerous invocations of small data movements if we use the cudaMemcpyAsync API. To mitigate the overhead, we implement a kernel that batches the copy operations for different blocks into a single kernel launch. 5.2 Supporting Various Decoding Algorithms vLLM implements various decoding algorithms using three key methods: fork, append, and free. The fork method creates a new sequence from an existing one. The append method appends a new token to the sequence. Finally, the free method deletes the sequence. For instance, in parallel sampling, vLLM creates multiple output sequences from the single input sequence using the fork method. It then adds new tokens to these sequences in every iteration with append, and deletes sequences that meet a stopping condition using free. The same strategy is also applied in beam search and prefix sharing by vLLM. We believe future decoding algorithms can also be supported by combining these methods. 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 5 Implementation vLLM is an end-to-end serving system with a FastAPI [15] frontend and a GPU-based inference engine. The frontend extends the OpenAI API [34] interface, allowing users to customize sampling parameters for each request, such as the maximum sequence length and the beam width 𝑘. The vLLM engine is written in 8.5K lines of Python and 2K lines of C++/CUDA code. We develop control-related components including the scheduler and the block manager in Python while developing custom CUDA kernels for key operations such as PagedAttention. For the model executor, we implement popular LLMs such as GPT [5], OPT [62], and LLaMA [52] using PyTorch [39] and Transformers [58]. We use NCCL [32] for tensor communication across the distributed GPU workers. 5.1 Kernel-level Optimization Since PagedAttention introduces memory access patterns that are not efficiently supported by existing systems, we develop several GPU kernels for optimizing it. (1) Fused reshape and block write. In every Transformer layer, the new KV cache are split into blocks, reshaped to a memory layout optimized for block read, then saved at positions specified by the block table. To minimize kernel launch overheads, we fuse them into a single kernel. (2) Fusing block read and attention. We adapt the attention kernel in FasterTransformer [31] to read KV cache according to the block table and perform attention operations on the fly. To ensure coalesced memory access, we assign a GPU warp to read each block. Moreover, we add support for variable sequence lengths within a request batch. (3) Fused block copy. Block copy operations, issued by the copy-on-write mechanism, may operate on discontinuous blocks. This can lead to numerous invocations of small data movements if we use the cudaMemcpyAsync API. To mitigate the overhead, we implement a kernel that batches the copy operations for different blocks into a single kernel launch. 5.2 Supporting Various Decoding Algorithms vLLM implements various decoding algorithms using three key methods: fork, append, and free. The fork method creates a new sequence from an existing one. The append method appends a new token to the sequence. Finally, the free method deletes the sequence. For instance, in parallel sampling, vLLM creates multiple output sequences from the single input sequence using the fork method. It then adds new tokens to these sequences in every iteration with append, and deletes sequences that meet a stopping condition using free. The same strategy is also applied in beam search and prefix sharing by vLLM. We believe future decoding algorithms can also be supported by combining these 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 Implements Decoding Algorithms

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Introduction The Data space is almost as volatile as the AI space this year, with many players consolidating. In the summer of 2022, I wrote about The Data Table Format Wars, and the ecosystem has only gotten more interesting since then. I won’t get deep into the weeds on Data Lakes vs. Data Warehouse, etc., but suffice it to say, we are seeing everything coalesce around decoupling data storage from computing. Table formats are a way to standardize lightly opinionated storage. As I opined a couple of years ago, Apache Iceberg seems to be where they are settling. These formats tend to use Apache Parquet on object storage and provide a method for tracking new data and data state changes. In 2023, Databricks announced the open-sourcing of their Unity Catalog, and Onehouse announced OneTable (now X-Table). These projects are meant to give you a single interface to the three popular table formats: Delta, Hudi, and Iceberg. Then, June 2024 happened… What Happened? Snowflake was having its annual Snowflake Summit conference, and on June 3, 2024, they announced Apache Polaris (incubating), a vendor-neutral, open catalog for Apache Iceberg. The next day, and a week before the Databricks conference, Databricks announced their acquisition of Tabular, a company founded by the creators of Iceberg. This still seems weird to everyone from the outside because Iceberg is a spec; it isn’t a product, and Tabular isn’t even the main contributor to Iceberg. In my opinion, Databricks will somehow roll their Deltalake into Iceberg. Behind the scenes, Snowflake had been trying to acquire Tabular. Something happened at the 11th hour, and Databricks came in and laid down a ton more money, and the Snowflake bid was discarded. Snowflake was already two years into their commitment to supporting Iceberg as external storage, which made a lot of sense for them regardless. Snowflake has a reputation concerning the release speed after an announcement, so there was some skepticism, and some even wondered if any code had been written. It turns out Snowflake had been working on this for a while, and it was made available in very short order, with vendors like Dremio, Starburst, CelerData, and Upsolver showing solutions using it in less than two months: a delightful surprise, and a positive sign of things to come. All of that is a long way to set up Apache Polaris, and hopefully, it will give you a sense of the importance that a good catalog plays in all of this. How will any of your compute tools work if you don’t have a unified system to describe all those parquet files in your data lake? Polaris Overview Apache Polaris is an open-source catalog implementation for Apache Iceberg tables. Its main features include: REST API implementation: Polaris implements Iceberg's REST API, enabling interoperability across multiple query engines.
Multi-engine compatibility: It allows various query engines, such as Apache Spark, Apache Flink, Trino, and others, to read and write Iceberg tables through a single catalog.
Centralized management: Polaris provides centralized and secure access to Iceberg tables across REST-compatible query engines.
Namespace and metadata management: It supports creating namespaces for logical grouping and manages metadata for Iceberg tables.
Storage configurations: Polaris offers robust storage configurations for cloud providers like S3, Azure, and Google Cloud Storage.
Security features: It implements role-based access control (RBAC) and uses credential vending to secure query execution.
Open-source nature: Polaris is fully open-source, allowing for community contributions and vendor neutrality.
Interoperability: It enables multiple engines to be used on a single copy of data, reducing storage duplication and ETL costs.
Consistent access control: Polaris allows you to manage role-based access control and storage layer security consistently across engines from one place.
Flexibility in hosting: It can be hosted on various infrastructures, providing flexibility and reducing vendor lock-in. And a lovely little graphic from the docs to put a picture on everything. Borrowing from the Polaris docs for a picture once again, let’s dive into what is happening here in this entity hierarchy graphic: We can have multiple catalogs in a single Polaris install; in this diagram, it is only Catalog1 that has any namespaces or tables defined. A single catalog can be either internal or external. The internal tables allow for read/write functionality, meaning you have full control. The external tables are currently read-only and are intended to mirror data that is in some external catalog, such as a Glue catalog, which Polaris will see as the source of truth once connected. The catalog organizes your iceberg tables; you’ll want to configure it with your storage configuration for the applicable cloud system that you are on. This forms the first architectural layer in the Iceberg table spec. A namespace can have an arbitrary level of nesting and is used to logically group Iceberg tables within a catalog. An Iceberg table will always belong to a namespace. With this configuration in place, you can then use the Polaris catalog to describe your Iceberg tables in such a way as to make them easily accessible and manageable using a large variety of tools. That’s a quick overview of what is happening and what it looks like. The implementation inside of Snowflake provides a really handy UI to manage the implementation; other vendors have their own, and you can just do it through Spark if you like. Summary I continue to believe that Apache Iceberg is going to “win” the table format wars. Apache Polaris focusing on Iceberg is not a bad thing. I want to dig into how RBAC has been implemented in Polaris another time and see if it is as robust as what the Tabular product implemented, which I was a big fan of. I believe Polaris is a big win for the Iceberg community and will help accelerate adoption even further. I’ve seen talk about the Dremio Nessie project getting rolled in, and I’m curious to see what that will look like. I haven’t looked at Nessie in a couple of years, and I thought their git-like semantics mainly had been covered by the Iceberg tagging and branching, which I wrote about last year—yet another story for another day. I’m a big fan of what is happening here. If you are in this space, this is the right train to jump on. Check out my other What the Heck is… articles at the links below: What The Heck Is DuckDB?
What the Heck Is Malloy?
What the Heck is PRQL?
What the Heck is GlareDB?
What the Heck is SeaTunnel?
What the Heck is LanceDB?
What the heck is SDF?
What the Heck is Paimon?
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What the Heck is WarpStream? Introduction Introduction The Data space is almost as volatile as the AI space this year, with many players consolidating. In the summer of 2022, I wrote about The Data Table Format Wars , and the ecosystem has only gotten more interesting since then. I won’t get deep into the weeds on Data Lakes vs. Data Warehouse, etc., but suffice it to say, we are seeing everything coalesce around decoupling data storage from computing. The Data Table Format Wars Table formats are a way to standardize lightly opinionated storage. As I opined a couple of years ago, Apache Iceberg seems to be where they are settling. These formats tend to use Apache Parquet on object storage and provide a method for tracking new data and data state changes. In 2023, Databricks announced the open-sourcing of their Unity Catalog, and Onehouse announced OneTable (now X-Table). These projects are meant to give you a single interface to the three popular table formats: Delta, Hudi, and Iceberg. Then, June 2024 happened… announced announced What Happened? Snowflake was having its annual Snowflake Summit conference, and on June 3, 2024, they announced Apache Polaris (incubating), a vendor-neutral, open catalog for Apache Iceberg. The next day, and a week before the Databricks conference, Databricks announced their acquisition of Tabular, a company founded by the creators of Iceberg. This still seems weird to everyone from the outside because Iceberg is a spec; it isn’t a product, and Tabular isn’t even the main contributor to Iceberg. In my opinion, Databricks will somehow roll their Deltalake into Iceberg. announced announced Behind the scenes, Snowflake had been trying to acquire Tabular. Something happened at the 11th hour, and Databricks came in and laid down a ton more money, and the Snowflake bid was discarded. Snowflake was already two years into their commitment to supporting Iceberg as external storage, which made a lot of sense for them regardless. Snowflake has a reputation concerning the release speed after an announcement, so there was some skepticism, and some even wondered if any code had been written. It turns out Snowflake had been working on this for a while, and it was made available in very short order, with vendors like Dremio, Starburst, CelerData, and Upsolver showing solutions using it in less than two months: a delightful surprise, and a positive sign of things to come. All of that is a long way to set up Apache Polaris , and hopefully, it will give you a sense of the importance that a good catalog plays in all of this. How will any of your compute tools work if you don’t have a unified system to describe all those parquet files in your data lake? Apache Polaris Polaris Overview Apache Polaris is an open-source catalog implementation for Apache Iceberg tables. Its main features include: REST API implementation: Polaris implements Iceberg's REST API, enabling interoperability across multiple query engines. Multi-engine compatibility: It allows various query engines, such as Apache Spark, Apache Flink, Trino, and others, to read and write Iceberg tables through a single catalog. Centralized management: Polaris provides centralized and secure access to Iceberg tables across REST-compatible query engines. Namespace and metadata management: It supports creating namespaces for logical grouping and manages metadata for Iceberg tables. Storage configurations: Polaris offers robust storage configurations for cloud providers like S3, Azure, and Google Cloud Storage. Security features: It implements role-based access control (RBAC) and uses credential vending to secure query execution. Open-source nature: Polaris is fully open-source, allowing for community contributions and vendor neutrality. Interoperability: It enables multiple engines to be used on a single copy of data, reducing storage duplication and ETL costs. Consistent access control: Polaris allows you to manage role-based access control and storage layer security consistently across engines from one place. Flexibility in hosting: It can be hosted on various infrastructures, providing flexibility and reducing vendor lock-in. REST API implementation: Polaris implements Iceberg's REST API, enabling interoperability across multiple query engines. Multi-engine compatibility: It allows various query engines, such as Apache Spark, Apache Flink, Trino, and others, to read and write Iceberg tables through a single catalog. Centralized management: Polaris provides centralized and secure access to Iceberg tables across REST-compatible query engines. Namespace and metadata management: It supports creating namespaces for logical grouping and manages metadata for Iceberg tables. Storage configurations: Polaris offers robust storage configurations for cloud providers like S3, Azure, and Google Cloud Storage. Security features: It implements role-based access control (RBAC) and uses credential vending to secure query execution. Open-source nature: Polaris is fully open-source, allowing for community contributions and vendor neutrality. Interoperability: It enables multiple engines to be used on a single copy of data, reducing storage duplication and ETL costs. Consistent access control: Polaris allows you to manage role-based access control and storage layer security consistently across engines from one place. Flexibility in hosting: It can be hosted on various infrastructures, providing flexibility and reducing vendor lock-in. And a lovely little graphic from the docs to put a picture on everything. Borrowing from the Polaris docs for a picture once again, let’s dive into what is happening here in this entity hierarchy graphic: We can have multiple catalogs in a single Polaris install; in this diagram, it is only Catalog1 that has any namespaces or tables defined. A single catalog can be either internal or external. The internal tables allow for read/write functionality, meaning you have full control. The external tables are currently read-only and are intended to mirror data that is in some external catalog, such as a Glue catalog, which Polaris will see as the source of truth once connected. The catalog organizes your iceberg tables; you’ll want to configure it with your storage configuration for the applicable cloud system that you are on. This forms the first architectural layer in the Iceberg table spec. A namespace can have an arbitrary level of nesting and is used to logically group Iceberg tables within a catalog. An Iceberg table will always belong to a namespace. With this configuration in place, you can then use the Polaris catalog to describe your Iceberg tables in such a way as to make them easily accessible and manageable using a large variety of tools. That’s a quick overview of what is happening and what it looks like. The implementation inside of Snowflake provides a really handy UI to manage the implementation; other vendors have their own, and you can just do it through Spark if you like. Summary I continue to believe that Apache Iceberg is going to “win” the table format wars. Apache Polaris focusing on Iceberg is not a bad thing. I want to dig into how RBAC has been implemented in Polaris another time and see if it is as robust as what the Tabular product implemented, which I was a big fan of. I believe Polaris is a big win for the Iceberg community and will help accelerate adoption even further. I’ve seen talk about the Dremio Nessie project getting rolled in, and I’m curious to see what that will look like. I haven’t looked at Nessie in a couple of years, and I thought their git-like semantics mainly had been covered by the Iceberg tagging and branching, which I wrote about last year—yet another story for another day. wrote about I’m a big fan of what is happening here. If you are in this space, this is the right train to jump on. Check out my other What the Heck is… articles at the links below: What The Heck Is DuckDB? What the Heck Is Malloy? What the Heck is PRQL? What the Heck is GlareDB? What the Heck is SeaTunnel? What the Heck is LanceDB? What the heck is SDF? What the Heck is Paimon? What the Heck is Proton? What the Heck is PuppyGraph? What the Heck is GPTScript? What the Heck is WarpStream? What The Heck Is DuckDB? What The Heck Is DuckDB? What the Heck Is Malloy? What the Heck Is Malloy? What the Heck is PRQL? What the Heck is PRQL? What the Heck is GlareDB? What the Heck is GlareDB? What the Heck is SeaTunnel? What the Heck is SeaTunnel? What the Heck is LanceDB? What the Heck is LanceDB? What the heck is SDF? What the heck is SDF? What the Heck is Paimon? What the Heck is Paimon? What the Heck is Proton? What the Heck is Proton? What the Heck is PuppyGraph? What the Heck is PuppyGraph? What the Heck is GPTScript? What the Heck is GPTScript? What the Heck is WarpStream? What the Heck is WarpStream?

What The Heck is Apache Polaris?

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(6) Carlo C Del Mundo;
(7) Mohammad Rastegari;
(8) Mehrdad Farajtabar. Table of Links Abstract and 1. Introduction 2. Flash Memory & LLM Inference and 2.1 Bandwidth and Energy Constraints 2.2 Read Throughput 3 Load From Flash 3.1 Reducing Data Transfer 3.2 Improving Transfer Throughput with Increased Chunk Sizes 3.3 Optimized Data Management in DRAM 4 Results 4.1 Results for OPT 6.7B Model 4.2 Results for Falcon 7B Model 5 Related Works 6 Conclusion and Discussion, Acknowledgements and References 2.2 Read Throughput Flash memory systems perform optimally with large sequential reads. For instance, benchmarks on an Apple MacBook Pro M2 with 2TB flash demonstrate speeds exceeding 6GiB/s for a 1GiB linear read of an uncached file. However, this high bandwidth is not replicated for smaller, random reads due to the inherent multi-phase nature of these reads, encompassing the operating system, drivers, interrupt handling, and the flash controller, among others. Each phase introduces latency, disproportionately affecting smaller reads. To circumvent these limitations, we advocate two primary strategies, which can be employed jointly. The first involves reading larger chunks of data. For smaller blocks, a substantial part of the overall read time is spent waiting for data transfer to begin. This is often referred to as latency to first byte. This latency reduces the overall throughput of each read operation considerably, because the overall measured throughput has to take into account not just the speed of transfer once it begins, but the latency before it begins as well, which penalizes small reads. This means that if we coalesce the reads for rows and colums of the FFN matrices, we can pay the latency cost only once for any given row/column pair in both matrices, and higher throughput can be realized. This principle is depicted in Figure 2b. Perhaps a counterintuitive yet interesting observation is that in some scenarios, it will be worthwhile to read more than needed (but in larger chunks) and then discard, than only reading strictly the necessary parts but in smaller chunks. The second strategy leverages parallelized reads, utilizing the inherent parallelism within storage stacks and flash controllers. Our results indicate that throughputs appropriate for sparse LLM inference are achievable on modern off-the-shelf hardware using 32KiB or larger random reads across multiple threads. Motivated by the challenges described in this section, in section 3, we propose methods to optimize data transfer volume and enhance read throughput to significantly enhance inference speeds. This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: (1) Keivan Alizadeh; (2) Iman Mirzadeh, Major Contribution; (3) Dmitry Belenko, Major Contribution; (4) S. Karen Khatamifard; (5) Minsik Cho; (6) Carlo C Del Mundo; (7) Mohammad Rastegari; (8) Mehrdad Farajtabar. Authors: Authors: (1) Keivan Alizadeh; (2) Iman Mirzadeh, Major Contribution; (3) Dmitry Belenko, Major Contribution; (4) S. Karen Khatamifard; (5) Minsik Cho; (6) Carlo C Del Mundo; (7) Mohammad Rastegari; (8) Mehrdad Farajtabar. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Flash Memory & LLM Inference and 2.1 Bandwidth and Energy Constraints 2. Flash Memory & LLM Inference and 2.1 Bandwidth and Energy Constraints 2.2 Read Throughput 2.2 Read Throughput 3 Load From Flash 3 Load From Flash 3.1 Reducing Data Transfer 3.1 Reducing Data Transfer 3.2 Improving Transfer Throughput with Increased Chunk Sizes 3.2 Improving Transfer Throughput with Increased Chunk Sizes 3.3 Optimized Data Management in DRAM 3.3 Optimized Data Management in DRAM 4 Results 4 Results 4.1 Results for OPT 6.7B Model 4.1 Results for OPT 6.7B Model 4.2 Results for Falcon 7B Model 4.2 Results for Falcon 7B Model 5 Related Works 5 Related Works 6 Conclusion and Discussion, Acknowledgements and References 6 Conclusion and Discussion, Acknowledgements and References 2.2 Read Throughput Flash memory systems perform optimally with large sequential reads. For instance, benchmarks on an Apple MacBook Pro M2 with 2TB flash demonstrate speeds exceeding 6GiB/s for a 1GiB linear read of an uncached file. However, this high bandwidth is not replicated for smaller, random reads due to the inherent multi-phase nature of these reads, encompassing the operating system, drivers, interrupt handling, and the flash controller, among others. Each phase introduces latency, disproportionately affecting smaller reads. To circumvent these limitations, we advocate two primary strategies, which can be employed jointly. The first involves reading larger chunks of data. For smaller blocks, a substantial part of the overall read time is spent waiting for data transfer to begin. This is often referred to as latency to first byte. This latency reduces the overall throughput of each read operation considerably, because the overall measured throughput has to take into account not just the speed of transfer once it begins, but the latency before it begins as well, which penalizes small reads. This means that if we coalesce the reads for rows and colums of the FFN matrices, we can pay the latency cost only once for any given row/column pair in both matrices, and higher throughput can be realized. This principle is depicted in Figure 2b. Perhaps a counterintuitive yet interesting observation is that in some scenarios, it will be worthwhile to read more than needed (but in larger chunks) and then discard, than only reading strictly the necessary parts but in smaller chunks. The second strategy leverages parallelized reads, utilizing the inherent parallelism within storage stacks and flash controllers. Our results indicate that throughputs appropriate for sparse LLM inference are achievable on modern off-the-shelf hardware using 32KiB or larger random reads across multiple threads. Motivated by the challenges described in this section, in section 3, we propose methods to optimize data transfer volume and enhance read throughput to significantly enhance inference speeds. This paper is available on arxiv under CC BY-SA 4.0 DEED license. This paper is available on arxiv under CC BY-SA 4.0 DEED license. available on arxiv

Large Language Models on Memory-Constrained Devices Using Flash Memory: Read Throughput

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