Researchers have demonstrated a method for cracking the Akira ransomware's encryption using sixteen RTX 4090 GPUs. By exploiting a vulnerability in Akira's implementation of the ChaCha20 encryption algorithm, they were able to brute-force the 256-bit encryption key in approximately ten hours. This breakthrough signifies a potential weakness in the ransomware and offers a possible recovery route for victims, though the required hardware is expensive and not readily accessible to most. The attack relies on Akira's flawed use of a 16-byte (128-bit) nonce, effectively reducing the key space and making it susceptible to this brute-force approach.
This paper explores Karatsuba matrix multiplication as a lower-complexity alternative to Strassen's algorithm, particularly for hardware implementations. It proposes optimized Karatsuba formulations for 2x2, 3x3, and 4x4 matrices, aiming to reduce the number of multiplications and additions required. The authors then introduce efficient hardware architectures for these formulations, leveraging parallelism and resource sharing to achieve high throughput and low latency. They compare their designs with existing Strassen-based implementations, demonstrating competitive performance with significantly reduced hardware complexity, making Karatsuba a viable option for resource-constrained environments like embedded systems and FPGAs.
HN users discuss the practical implications of the Karatsuba algorithm for matrix multiplication, questioning its real-world advantages over Strassen's algorithm, especially given the overhead of recursion and the complexities of hardware implementation. Some express skepticism about achieving the claimed performance gains, citing Strassen's wider adoption and existing optimized implementations. Others point out the potential benefits of Karatsuba in specific contexts like embedded systems or systolic arrays, where its simpler structure might be advantageous. The discussion also touches upon the challenges of implementing efficient hardware for either algorithm and the need to consider factors like memory access patterns and data dependencies. A few commenters highlight the theoretical interest of the paper and the potential for further optimizations.
Computational lithography, crucial for designing advanced chips, relies on computationally intensive simulations. Using CPUs for these simulations is becoming increasingly impractical due to the growing complexity of chip designs. GPUs, with their massively parallel architecture, offer a significant speedup for these workloads, especially for tasks like inverse lithography technology (ILT) and model-based OPC. By leveraging GPUs, chipmakers can reduce the time required for mask optimization, leading to faster design cycles and potentially lower manufacturing costs. This allows for more complex designs to be realized within reasonable timeframes, ultimately contributing to advancements in semiconductor technology.
Several Hacker News commenters discussed the challenges and complexities of computational lithography, highlighting the enormous datasets and compute requirements. Some expressed skepticism about the article's claims of GPU acceleration benefits, pointing out potential bottlenecks in data transfer and the limitations of GPU memory for such massive simulations. Others discussed the specific challenges in lithography, such as mask optimization and source-mask optimization, and the various techniques employed, like inverse lithography technology (ILT). One commenter noted the surprising lack of mention of machine learning, speculating that perhaps it is already deeply integrated into the process. The discussion also touched on the broader semiconductor industry trends, including the increasing costs and complexities of advanced nodes, and the limitations of current lithography techniques.
This blog post details setting up a bare-metal Kubernetes cluster on NixOS with Nvidia GPU support, focusing on simplicity and declarative configuration. It leverages NixOS's package management for consistent deployments across nodes and uses the toolkit's modularity to manage complex dependencies like CUDA drivers and container toolkits. The author emphasizes using separate NixOS modules for different cluster components—Kubernetes, GPU drivers, and container runtimes—allowing for easier maintenance and upgrades. The post guides readers through configuring the systemd unit for the Nvidia container toolkit, setting up the necessary kernel modules, and ensuring proper access for Kubernetes to the GPUs. Finally, it demonstrates deploying a GPU-enabled pod as a verification step.
Hacker News users discussed various aspects of running Nvidia GPUs on a bare-metal NixOS Kubernetes cluster. Some questioned the necessity of NixOS for this setup, suggesting that its complexity might outweigh its benefits, especially for smaller clusters. Others countered that NixOS provides crucial advantages for reproducible deployments and managing driver dependencies, particularly valuable in research and multi-node GPU environments. Commenters also explored alternatives like using Ansible for provisioning and debated the performance impact of virtualization. A few users shared their personal experiences, highlighting both successes and challenges with similar setups, including issues with specific GPU models and kernel versions. Several commenters expressed interest in the author's approach to network configuration and storage management, but the author didn't elaborate on these aspects in the original post.
The Fly.io blog post "We Were Wrong About GPUs" admits their initial prediction that smaller, cheaper GPUs would dominate the serverless GPU market was incorrect. Demand has overwhelmingly shifted towards larger, more powerful GPUs, driven by increasingly complex AI workloads like large language models and generative AI. Customers prioritize performance and fast iteration over cost savings, willing to pay a premium for the ability to train and run these models efficiently. This has led Fly.io to adjust their strategy, focusing on providing access to higher-end GPUs and optimizing their platform for these demanding use cases.
HN commenters largely agreed with the author's premise that the difficulty of utilizing GPUs effectively often outweighs their potential benefits for many applications. Several shared personal experiences echoing the article's points about complex tooling, debugging challenges, and ultimately reverting to CPU-based solutions for simplicity and cost-effectiveness. Some pointed out that specific niches, like machine learning and scientific computing, heavily benefit from GPUs, while others highlighted the potential of simpler GPU programming models like CUDA and WebGPU to improve accessibility. A few commenters offered alternative perspectives, suggesting that managed services or serverless GPU offerings could mitigate some of the complexity issues raised. Others noted the importance of right-sizing GPU instances and warned against prematurely optimizing for GPUs. Finally, there was some discussion around the rising popularity of ARM-based processors and their potential to offer a competitive alternative for certain workloads.
DeepSeek claims a significant AI performance boost by bypassing CUDA, the typical programming interface for Nvidia GPUs, and instead coding directly in PTX, a lower-level assembly-like language. This approach, they argue, allows for greater hardware control and optimization, leading to substantial speed improvements in their inference engine, Coder, specifically for large language models. While promising increased efficiency and reduced costs, DeepSeek's approach requires more specialized expertise and hasn't yet been independently verified. They are making their Coder software development kit available for developers to test these claims.
Hacker News commenters are skeptical of DeepSeek's claims of a "breakthrough." Many suggest that using PTX directly isn't novel and question the performance benefits touted, pointing out potential downsides like portability issues and increased development complexity. Some argue that CUDA already optimizes and compiles to PTX, making DeepSeek's approach redundant. Others express concern about the lack of concrete benchmarks and the heavy reliance on marketing jargon in the original article. Several commenters with GPU programming experience highlight the difficulties and limited advantages of working with PTX directly. Overall, the consensus seems to be that while interesting, DeepSeek's approach needs more evidence to support its claims of superior performance.
The openai-realtime-embedded-sdk allows developers to build AI assistants that run directly on microcontrollers. This SDK bridges the gap between OpenAI's powerful language models and resource-constrained embedded devices, enabling on-device inference without relying on cloud connectivity or constant internet access. It achieves this through quantization and compression techniques that shrink model size, allowing them to fit and execute on microcontrollers. This opens up possibilities for creating intelligent devices with enhanced privacy, lower latency, and offline functionality.
Hacker News users discussed the practicality and limitations of running large language models (LLMs) on microcontrollers. Several commenters pointed out the significant resource constraints, questioning the feasibility given the size of current LLMs and the limited memory and processing power of microcontrollers. Some suggested potential use cases where smaller, specialized models might be viable, such as keyword spotting or limited voice control. Others expressed skepticism, arguing that the overhead, even with quantization and compression, would be too high. The discussion also touched upon alternative approaches like using microcontrollers as interfaces to cloud-based LLMs and the potential for future hardware advancements to bridge the gap. A few users also inquired about the specific models supported and the level of performance achievable on different microcontroller platforms.
Summary of Comments ( 11 )
https://news.ycombinator.com/item?id=43387188
Hacker News commenters discuss the practicality and implications of using RTX 4090 GPUs to crack Akira ransomware. Some express skepticism about the real-world applicability, pointing out that the specific vulnerability exploited in the article is likely already patched and that criminals will adapt. Others highlight the increasing importance of strong, long passwords given the demonstrated power of brute-force attacks with readily available hardware. The cost-benefit analysis of such attacks is debated, with some suggesting the expense of the hardware may be prohibitive for many victims, while others counter that high-value targets could justify the cost. A few commenters also note the ethical considerations of making such cracking tools publicly available. Finally, some discuss the broader implications for password security and the need for stronger encryption methods in the future.
The Hacker News post titled "Akira ransomware can be cracked with sixteen RTX 4090 GPUs in around ten hours" has generated several comments discussing the implications of using powerful GPUs like the RTX 4090 for cracking encryption.
Some users express skepticism about the practicality of this approach. One commenter questions the feasibility for average users, pointing out the significant cost of acquiring sixteen RTX 4090 GPUs. They suggest that while technically possible, the financial barrier makes it unlikely for most victims of ransomware. Another user echoes this sentiment, highlighting that the cost would likely exceed the ransom demand in many cases. They also raise the point that this method might only work for a specific vulnerability in Akira and wouldn't be a universal solution for all ransomware.
Others discuss the broader implications of readily available GPU power. One comment points out the increasing accessibility of powerful hardware and its potential to empower both security researchers and malicious actors. They argue that this development underscores the ongoing "arms race" in cybersecurity, where advancements in technology benefit both sides. Another user suggests that this highlights the importance of robust encryption practices, as the increasing power of GPUs could eventually render weaker encryption methods vulnerable.
A few comments delve into the technical aspects. One user questions the specific algorithm used by Akira and speculates on its susceptibility to brute-force attacks. Another user mentions the importance of key length and how it affects the time required for cracking, emphasizing that longer keys would significantly increase the difficulty even with powerful GPUs.
One commenter points out the article's potentially misleading title. They clarify that the GPUs weren't cracking the encryption itself, but rather brute-forcing a password which was then used to decrypt the files. This distinction is important, as it implies a weakness in the implementation rather than the underlying encryption algorithm.
Finally, a few users offer practical advice. One suggests using strong, unique passwords to protect against this type of attack, emphasizing the importance of basic security hygiene. Another user proposes that the best defense against ransomware remains regular backups, allowing victims to restore their data without paying the ransom.
Overall, the comments reflect a mix of concerns about the practical implications of using GPUs for cracking ransomware, discussions about the broader cybersecurity landscape, and technical insights into the vulnerabilities highlighted by this specific case.