Update Proof of Time

Proof-of-Time.md

@@ -21,14 +21,3 @@ GPUs (Graphics Processing Units) became the preferred hardware for PoW mining du
 As difficulty levels in blockchain networks like Bitcoin increased, the computational power provided by CPUs quickly became insufficient for effective mining. GPUs, with their superior parallel processing capabilities, offered a significant performance boost, enabling miners to generate more hashes in a shorter period and thus increasing their likelihood of discovering a valid block. However, as the blockchain ecosystem evolved, specialized hardware such as ASICs (Application-Specific Integrated Circuits) eventually outpaced GPUs, leading to the centralization of mining power in PoW networks.
 
 
-### Proof of Work (RandomX)
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-RandomX is a PoW concept that was created by Monero. It is specifically designed to be CPU-friendly and resistant to the dominance of specialized mining hardware like GPUs and ASICs. RandomX was introduced to preserve decentralization by making mining feasible on consumer-grade CPUs while limiting the effectiveness of GPUs and ASICs. This approach contrasts with traditional PoW algorithms.
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-RandomX is a memory-intensive and compute-intensive PoW algorithm that leverages features of general-purpose CPUs to perform mining tasks efficiently. The core idea behind RandomX is to design an algorithm that can fully utilize the strengths of modern CPUs, such as branch prediction, out-of-order execution, and memory access patterns. This it difficult to optimize these processes on GPUs and ASICs. It achieves this by relying on random code execution and memory-hard operations, which require large amounts of RAM and frequent memory access. This makes it prohibitively expensive and inefficient for specialized hardware to outperform CPUs in the mining process.
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-The key difference between Monero’s RandomX and traditional PoW algorithms, like Bitcoin’s SHA-256 or Litecoin’s Scrypt, lies in the way these algorithms interact with hardware. In traditional PoW, the computational workload is focused on repetitive hashing operations, which can be parallelized easily, giving GPUs and ASICs a significant edge. 
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-In contrast, RandomX is deliberately designed to take advantage of the sequential processing capabilities of CPUs. The algorithm dynamically generates random sequences of instructions and requires large amounts of memory, limiting the degree of parallelism that GPUs can achieve. GPUs, which excel at performing many simple tasks in parallel, struggle with RandomX’s sequential and memory-intensive workload.
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-RandomX has an inverse effect compared to traditional PoW algorithms. While traditional PoW leads to mining centralization around GPUs and ASIC hardware, RandomX shifts the focus to CPUs and memory, making CPU mining more efficient. However, like other PoW algorithms, it still favors one type of hardware—CPUs—over GPUs and ASICs. To date, no PoW algorithm has been able to balance mining equally across all hardware types, as each algorithm inevitably favors either CPUs or specialized hardware like GPUs and ASICs.