Argon2 relating to memory and difficulty
Mining capacity bounded by the VRAM's speed.
Last updated
Mining capacity bounded by the VRAM's speed.
Last updated
Argon2 is the designated cryptographic hashing algorithm for mining XenBlocks. It features dynamic difficulty adjustment that applies uniformly across all miners. Naturally resistant to GPU and ASIC mining, "resistant" in this context doesn't mean impossible; rather, it implies a higher degree of difficulty and cost. The reason behind this resistance is Argon2's substantial demand for memory or VRAM allocation. As the mining difficulty escalates, the rate of hash generation decreases, highlighting the algorithm's memory-intensive nature. Therefore, Argon2 is categorized as a memory-hard algorithm, directly tying its operation and efficiency to the utilization of memory and RAM.
Random Access Memory (RAM) is a crucial component of a computer that temporarily stores data needed while the computer is operating. This data can range from a document you're working on to the webpage you're currently viewing. When the computer requires this data, it retrieves it swiftly and effortlessly from the RAM, facilitating smooth and efficient operation.
VRAM, or Video RAM, is a specialized form of RAM designed expressly for handling graphics processing tasks. While general-purpose RAM serves as the primary memory for your computer, facilitating data storage for current computations by the processor across various applications, VRAM is allocated specifically for the graphics processor. This dedicated memory allows the graphics processor to store and manage data related to visual computations, enhancing the performance of rendering images, videos, and graphics-intensive applications. Essentially, VRAM is to the graphics processor what RAM is to the central processor, but with a focus on optimizing graphics performance.
As a miner, the GPU, or Graphics Processing Unit, is crucial for generating hash power. Initially designed to boost computer graphics and image processing, GPUs have proven to be highly effective for mining XenBlocks (and other cryptocurrencies) due to their substantial hash power capabilities.
To mine a block, you must utilize VRAM, the video-specific RAM, indicating that your mining capacity is bounded by the VRAM's speed. The critical factor here is not merely how quickly you can discover blocks, but the time it takes to access VRAM—the bandwidth or data transfer rate significantly influences mining difficulty.
Therefore, VRAM speed emerges as the bottleneck in this process. For instance, with a difficulty level of 90K, which approximates to accessing 100MB of data, the time required to mine a block is determined by how fast you can access this 100MB. Should the difficulty increase to 200K, implying the need to access 200MB of data, the time required could potentially double. While this relationship isn't strictly linear, it serves to illustrate that your mining efficiency is governed by bandwidth rather than your computational prowess in solving hashes.
As the difficulty escalates, requiring more bandwidth to mine a block, the demand on memory access intensifies. This characteristic is why Argon2 is described as "memory hard"—it underscores the importance of memory access, a resource that is inherently finite, in the mining process.
The efficiency of mining using Argon2 is primarily constrained by the speed of VRAM. Here's a breakdown of the speeds associated with a few VRAM types:
GDDR5
1 GB/s - 1.75 GB/s
Common in older or budget graphics cards
GDDR5X
1.25 GB/s - 1.75 GB/s
An improved version of GDDR5
GDDR6
1.75 GB/s - 2 GB/s
Found in many current mid-range to high-end graphics cards
GDDR6X
2.375 GB/s - 2.625 GB/s
The latest and fastest GDDR technology, used in top-tier GPUs
The actual mining performance and effectiveness for Argon2 would depend on the VRAM's speed and how the GPU architecture utilizes it.
NVIDIA RTX 4000 series and the NVIDIA A40 are examples of GPUs proven to be very efficient in output versus cost when it comes to XenBlocks minnig.
NVIDIA RTX 4000 Series (e.g., GeForce RTX 4090, part of the RTX 40 series): This series primarily uses GDDR6X VRAM, known for its high speed and efficiency. GDDR6X offers significant improvements over previous generations, like GDDR6, in terms of bandwidth and power efficiency, making it well-suited for gaming and high-performance computing tasks. For instance, the GeForce RTX 4090 uses GDDR6X memory.
NVIDIA A40: The NVIDIA A40 is designed for professional and data center applications, and it utilizes GDDR6 memory. The A40 is tailored for AI, deep learning, and high-performance computing (HPC) workloads, offering a balance between high memory capacity and speed to support complex computational tasks in professional environments.
GeForce RTX 40 series, such as the RTX 4090, utilizes GDDR6X VRAM, and for the RTX 4090 specifically, the memory speed can reach up to 21.2 Gbps per pin. Given its 384-bit memory interface, this translates to a bandwidth of about 1,008 GB/s.
Each type of VRAM serves the specific needs of its respective GPU's target application, with GDDR6X focusing on maximizing throughput for gaming and graphical tasks, while GDDR6 in professional GPUs like the A40 emphasizes a balance between speed and capacity for compute-intensive application. Imagine you have GDDR6X VRAM that operates at a speed of 2.65 GB/s. So, taking a scenario where the difficulty level corresponds to processing 100MB of data (equivalent to a 90K difficulty level):
You can process 100MB of data 27.136 times per second. This means you can load 27.136 sets of 100MB into that memory each second.
If the difficulty increases to a level requiring the processing of 200MB of data (equivalent to a 200K difficulty level), you would be able to process those 200MB of data 13.568 times per second.
The frequency at which 100MB and 200MB data sets can be processed exhibits a 50% reduction when moving from the smaller to the larger data set. Specifically, the capacity to process 100MB of data 27.136 times per second drops to 13.568 times per second for 200MB. This demonstrates the direct effect of rising difficulty levels, which demand the management of increasingly large data sets, on the processing capability per second. It emphasizes the critical role of VRAM speed in determining the efficiency of mining operations.
Given a high-end GPU capable of achieving 5,000 hashes per second (h/s), the bottleneck isn't the hashing capability but rather the memory's capacity to handle the data required for those hashes. This constraint means that even with significant computational power, your effective hash rate is limited by how quickly the memory can supply data.
This limitation also illustrates why achieving terahash levels of performance is not feasible with current RAM speeds; the memory access speed becomes a critical bottleneck. Even with an abundance of computational power (hash power), the finite capacity and speed of memory restrict how quickly blocks can be produced. As the Argon2 algorithm is memory-hard, meaning it intensifies memory requirements as difficulty increases, memory access speed and capacity fundamentally limit mining efficiency.
Therefore, the challenges posed by memory constraints make ASIC mining—a technique relying on specialized hardware designed for maximum efficiency in specific computational tasks—less likely to be effective for mining algorithms like Argon2, where memory speed and capacity are the primary limiting factors.
