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TeamRedMiner Mining Protocol Message Processing: A Technical Analysis of Epoch, Target, and Extranonce HandlingTeamRedMiner is an optimized AMD GPU and FPGA cryptocurrency miner that implements sophisticated protocol handling for communicating with mining pools using the Stratum protocol[1][2]. At the core of its mining operation lies the critical task of parsing mining protocol messages, particularly the mining.set notification, which carries essential parameters including epoch, target, and extranonce values that must be precisely translated into GPU-executable instructions. Understanding how TeamRedMiner processes these parameters reveals the intricate relationship between pool-side difficulty distribution and GPU-level hash computation, demonstrating why the miner converts target values into local share difficulty thresholds for efficient work distribution across compute devices.Stratum Protocol Fundamentals and Mining.Set Message ArchitectureThe Stratum protocol represents a fundamental communication interface between mining clients and pool servers, establishing a JSON-RPC based framework that has evolved significantly since its initial deployment for Bitcoin mining[26]. TeamRedMiner implements support for multiple Stratum variants including the traditional Stratum V1 protocol, the newer Ethereum Stratum 2.0 specification, and proprietary protocol extensions for various blockchain networks[1][22]. The mining.set message, which is the server-to-client notification method, carries critical session-level parameters that the miner must interpret and apply to ongoing mining operations[8][14].In the Stratum protocol specification, the mining.set notification is designed to push dynamic updates to miners regarding changes in session parameters[11][17]. The protocol maintains server-side state tracking for seed values, target values, and extranonce assignments, with the server responsible for notifying clients whenever these values change[11]. For TeamRedMiner, which must support both CPU-side work validation and GPU-side hash generation, the mining.set message represents a critical control point where pool-determined parameters are translated into hardware-specific configurations. The message structure includes multiple data elements that must be parsed in a specific sequence, with the order and interpretation dependent on the underlying blockchain network's proof-of-work algorithm[8][14].Epoch Parsing and DAG ManagementThe epoch parameter in the mining.set message is fundamentally connected to the Ethash proof-of-work algorithm's memory-hard properties, making its correct parsing essential for maintaining mining continuity[55]. Ethash uses a Directed Acyclic Graph (DAG) dataset that grows progressively with each epoch, calculated as the block height divided by 30,000 for Ethereum and ETC mining[55][58]. TeamRedMiner implements sophisticated epoch tracking because the DAG size directly impacts GPU memory allocation and kernel compilation strategies[4][9].When TeamRedMiner receives an epoch value through mining.set, it must determine whether this epoch differs from the currently active mining epoch and whether the corresponding DAG is already cached in GPU memory[13]. The miner implements the argument --eth_dag_cache which allows prebuilding and caching DAG datasets for specific epochs to minimize the latency associated with DAG regeneration during epoch transitions[1][13]. For eth+zil mining configurations, TeamRedMiner users are recommended to use --eth_dag_cache=0, which enables caching of the epoch 0 DAG specifically to avoid stall periods when pools perform algorithm switching[1][13].The epoch parsing logic must handle edge cases specific to different blockchain networks. For example, Ethereum Classic implements etchash at epoch 390 and beyond according to ECIP-1099, requiring TeamRedMiner to track epoch boundaries and switch mining modes accordingly[1][4]. The miner provides --eth_variant_mode arguments including "pool", "etchash", "deduce", "auto", "ethash", and "force_etc" modes to handle this complexity[1][13]. In "pool" mode, the miner relies on the server to send algo flags indicating whether etchash mode should be activated, effectively delegating epoch-based algorithm selection to the pool[1][13].For memory allocation, TeamRedMiner uses the epoch value to determine the maximum DAG size it must accommodate. The argument --eth_alloc_epoch=N allows users to pre-allocate GPU memory sufficient for a specific epoch, which is particularly important for 4GB graphics cards where memory constraints severely limit mining viability[1][9][13]. The miner calculates memory requirements based on the formula that DAG size grows by approximately 13-14 MB per epoch, starting from an initial 1GB at epoch 0[58]. When the miner receives an epoch value higher than the pre-allocated capacity, it must request additional memory from the GPU driver, a process that can cause brief stalls in mining operations[4][9].Target Parsing and Conversion to Local DifficultyThe target parameter transmitted through mining.set represents the pool's requested share difficulty threshold, expressed as a 256-bit hexadecimal number[8][14]. TeamRedMiner must parse this hexadecimal target string and convert it into computational difficulty metrics that both the GPU kernel and CPU validation routines can utilize. The relationship between target and difficulty is inverse: a higher numerical target value represents lower difficulty, while a lower target represents higher difficulty[25][37][39].The mathematical relationship between target and difficulty follows the formula: difficulty=targetmax_target​[37][44]. For Bitcoin-derived protocols, the maximum target is defined as 0x1d00ffff or approximately 0x00000000FFFF0000000000000000000000000000000000000000000000000000 when expanded[37][44]. TeamRedMiner converts the pool-provided target into a numerical difficulty value by computing the number of leading zero bits in the target, which inversely correlates with difficulty[28][40].When TeamRedMiner establishes this local difficulty threshold, it creates a "share difficulty" that represents the minimum hash value a GPU must find to submit a valid share to the pool[31][39][42]. This local difficulty differs from the network difficulty, which represents the actual difficulty required to find a valid blockchain block[31][39]. The share difficulty serves as an intermediate measurement point that allows pools to accurately measure miner hashrate without processing billions of individual hashes[31][42]. A miner with higher hashrate will receive a higher share difficulty target, ensuring consistent share submission rates across miners of varying power[31][39][42].TeamRedMiner's GPU kernels perform hash computation and compare the resulting hash against this target value directly at the hardware level[9][20]. The miner's ethash kernels generate hash values for each work unit and check whether the result falls below the target threshold[9]. Because modern GPUs produce hashes at extraordinary rates—potentially terahashes per second for high-end accelerators—setting an appropriate target value is critical for preventing pool overload from excessive share submission or conversely, insufficient measurement data for hashrate estimation[31][42].The target parameter also determines the internal work distribution within the GPU. TeamRedMiner can adjust kernel intensity parameters based on the target difficulty, allowing the miner to tune the number of parallel work items processed by each GPU to match pool requirements[9][13]. The argument --high_sample_mode=8 specifically lowers the internal difficulty by 256x for testing purposes, enabling rapid assessment of hardware error rates[9][20].Extranonce Parsing and Nonce Space ManagementThe extranonce parameter, transmitted through mining.set as part of the mining.set_extranonce method or as a session parameter in the initial subscription[26][29], represents the pool's unique identifier component for the nonce space assigned to this particular miner connection[8][14]. The extranonce concept extends the available nonce search space beyond the 32-bit nonce field in the Bitcoin block header, allowing pools to mathematically assign disjoint subspaces to different miners, preventing computational redundancy[8][36][57].TeamRedMiner receives extranonce in hexadecimal string format and must convert it to a binary representation for appending to the miner-generated nonce values during share submission[29][49]. The Stratum protocol specifies that extranonce and minernonce must be 8 bytes (64 bits) in total length, with the server freely choosing the length of the extranonce portion and the miner using the remaining bytes for its own nonce counter[8][14]. For example, if the pool assigns a 4-byte extranonce, TeamRedMiner can use the remaining 4 bytes to generate 2^32 unique nonce values before exhausting its assigned search space.The parsing logic must handle the variable-length extranonce correctly, as different pools implement different extranonce sizes[8][26]. TeamRedMiner stores the extranonce value in both its original string representation for logging and debugging, and as a numerical value for efficient computation during share generation[49]. When the miner constructs a share submission, it concatenates the extranonce with the miner-generated nonce portion in little-endian format, ensuring that the resulting 8-byte value uniquely represents work performed by this miner during this session[8][36].The protocol also specifies that pools can dynamically update the extranonce value through mining.set_extranonce messages, allowing the pool to reassign nonce space to a miner during an active connection[26][29]. TeamRedMiner must gracefully handle these reassignments, updating its internal nonce counter and ensuring that previously submitted shares with the old extranonce remain valid while new shares use the updated extranonce value[26][29].GPU Parameter Conversion and Kernel ConfigurationAfter parsing epoch, target, and extranonce from the mining.set message, TeamRedMiner must translate these pool-level parameters into GPU-specific kernel configurations and memory layouts[2][10]. This conversion process involves multiple intermediate steps where abstract mining parameters are mapped to concrete hardware operations.For the DAG (Directed Acyclic Graph) structure, the epoch value directly determines which pre-computed dataset the GPU kernels must use[55][58]. TeamRedMiner maintains multiple DAG buffers in GPU memory, potentially caching different epochs simultaneously for algorithms that require rapid switching between mining targets[1][13]. The miner's DAG allocation strategy uses the --eth_dag_buf argument to specify whether single or dual buffer modes should be employed[1][18][27]. In single buffer mode, the entire DAG for the current epoch is stored contiguously, maximizing memory bandwidth efficiency[1][27]. In dual buffer mode, the DAG is split across two non-contiguous memory regions, which can improve cache coherency on certain GPU architectures but generally consumes more memory[1][27].The target value undergoes conversion into GPU kernel parameters through a process that depends on the specific hardware architecture. For Polaris GPUs (RX 480/580), Vega GPUs (RX 56/64), Navi GPUs (RX 5700), and RDNA GPUs (RX 6000 series), TeamRedMiner implements architecture-specific kernel optimizations with varying support for mining modes[2][10]. The miner automatically selects between A-mode (aggressive), B-mode (balanced), and C-mode (computation-intensive) kernels based on the GPU model and user configuration[2][9][13]. Each mode processes the target value differently in terms of kernel intensity parameters, which determine the number of work items launched per kernel invocation.The kernel intensity parameter, represented in TeamRedMiner configuration as [L] in expressions like --eth_config=A750, determines the parallelism level within GPU execution[1][13][18]. The intensity value is calculated as a multiple of the GPU's compute unit count, with Polaris supporting intensity ranges from 0 to 384 (for RX 470/570) or 0 to 432 (for RX 480/580)[9]. Navi GPUs support intensities up to 384 for 8GB variants and higher for 16GB cards[9]. The target value, through its impact on share discovery rates, influences the optimal intensity setting: a higher target (lower difficulty) requires less GPU parallelism to achieve target share submission rates, while a lower target (higher difficulty) demands maximum parallelism.Target as Local Difficulty: Architectural RationaleTeamRedMiner establishes the target as the local difficulty threshold because this represents the most efficient and verifiable way to measure GPU work output in a pooled mining environment[31][39][42]. The relationship between target difficulty and GPU utilization is fundamental: every hash produced by the GPU kernel is compared against the target value, and only hashes meeting the difficulty threshold are counted as valid shares[31][39][42].The architectural choice to use target as local difficulty stems from the asymmetry between GPU capability and pool infrastructure capacity[31][42]. A single modern GPU can produce terahashes per second, but the pool's share validation infrastructure can only process a limited number of share submissions per second[31][42]. If the pool were to measure hashrate by counting raw hashes instead of filtered shares, the pool would be overwhelmed with validation work[31][42]. Instead, by setting a share difficulty target, the pool effectively instructs the GPU to filter its output, submitting only hashes that meet the target threshold[31][39].This filtering mechanism translates directly into computational validation requirements. If a GPU produces one terahash per second and submits one share every two seconds on average, the pool knows that the miner performed approximately two terahashes of work[31][42]. The pool can verify each share by recomputing the hash and confirming it meets the difficulty threshold, then credits the miner with the appropriate number of shares based on the verified difficulty[31][42]. A share discovered at twice the network difficulty is worth twice as many shares as a share at baseline difficulty[31][42].TeamRedMiner's implementation of this principle involves comparing every GPU-generated hash against the target value before submission[9][20]. The GPU kernel itself can be configured to pre-filter hashes within the hardware, reducing CPU overhead, or CPU-side validation can check every hash submitted by the GPU[9][20]. For testing purposes with --high_sample_mode=8, TeamRedMiner lowers the effective target 256-fold, causing the GPU to generate 256x more shares that pass the local filter, allowing rapid assessment of hardware stability and error rates[9][20].The target-as-difficulty approach also enables dynamic difficulty adjustment. When a miner's hashrate changes significantly, the pool can issue a new target through mining.set_target, causing the miner to filter shares at a new difficulty level without interrupting mining operations[26][38]. TeamRedMiner implements this flexibility through the --eth_hashwatch argument, which monitors actual hashrate and alerts operators if performance deviates significantly from expected values based on the current difficulty target[1][23][27].Stratum Protocol Implementation Details in TeamRedMinerTeamRedMiner's Stratum protocol implementation must handle numerous edge cases and protocol variations that affect how mining.set messages are interpreted[1][3][7][43]. The miner supports multiple pool connection strategies through the --pool_strategy argument, which can be set to "priority", "load_balance", "quota", or "min_epoch"[1][43]. Each strategy affects how the miner prioritizes work from multiple pools when mining.set messages contain different parameters across pools.The miner implements protocol-level extranonce subscription handling through the --pool_force_ensub and --pool_no_ensub arguments[1][43]. Some pools require explicit subscription to extranonce update messages, while others implement the Nicehash variant of the protocol which uses different extranonce handling semantics[1][29][43]. TeamRedMiner must automatically detect pool type and configure appropriate message sequences[1][3].For Ethereum-specific mining, TeamRedMiner implements specialized stratum mode detection through --eth_stratum_mode, supporting "stratum", "nicehash", and "ethproxy" modes[1][13][23][43]. The ethproxy mode is used by some older pool implementations and requires different parameter encoding for epoch, target, and extranonce compared to standard Stratum[1][13]. TeamRedMiner's automatic detection attempts to identify the correct mode by analyzing pool responses, but users can force a specific mode if detection fails[1][13][23].The miner also implements --pool_broken_rpc for pools that violate the JSON-RPC specification regarding request IDs, a workaround that limits outstanding RPC requests to one at a time, preventing protocol confusion[1][43]. Additionally, --pool_comb_sub_auth handles pools that require subscription and authorization messages to be sent as a batch rather than sequentially[1][43].Memory Architecture and DAG Caching ImplementationTeamRedMiner's memory management around epoch and DAG handling involves sophisticated caching algorithms that directly impact mining performance during epoch transitions[4][10]. The miner's DAG cache implementation uses the --eth_dag_cache argument to enable saving DAG datasets for multiple epochs simultaneously, preventing the need to regenerate DAGs when pools switch between different mining targets[1][13][23].When an epoch change occurs in mining.set, TeamRedMiner first checks whether the required DAG already exists in the cache[4][13]. If cached, the miner simply loads the DAG into GPU memory with minimal latency[4][13]. If not cached, the miner must generate the DAG from the cache dataset, a process that takes several seconds and temporarily suspends mining[4][13]. The --eth_dag_cache_resv argument allows users to specify how much GPU memory should be reserved for DAG caching operations, enabling larger caches on systems with abundant VRAM[1][13].For eth+zil switching configurations, TeamRedMiner automatically sets up dual DAG caching by using epoch 0 for ZIL mining and the current Ethereum epoch for ETH mining[1][12][27]. The R-mode support for dual mining with ZIL represents an optimization where the miner uses special compute modes to efficiently manage memory between the two algorithms' different DAG requirements[2][10].The memory allocation strategy accounts for the steadily growing DAG size across epochs. The argument --eth_alloc_epoch=N allows pre-allocation for epoch N, with the miner attempting to use single allocations on recent drivers that support allocations larger than 4GB[1][9]. For 4GB GPUs, TeamRedMiner implements the --eth_4g_max_alloc feature, which extends mining capability to epochs beyond what the full DAG would fit, by reducing internal memory buffers and accepting some hashrate penalty[1][9][13].The miner maintains separate DAG buffers for different mining modes. A-mode kernels use the standard DAG layout, while B-mode and C-mode kernels may use alternative memory access patterns that require different DAG organization[9][20]. The mining.set epoch parameter triggers reconfiguration of these buffers only when the epoch actually changes, minimizing computational overhead for repeated jobs from the same epoch[4][13].Target-Based Work Distribution and Share SubmissionThe target value parsed from mining.set directly impacts how TeamRedMiner distributes work across GPU compute units and validates share submissions[9][20]. The GPU kernels implement hash computation loops that iterate through candidate nonces, computing the ethash hash for each combination of header, nonce, and DAG, then comparing the result against the target[9][20][55].Ethash kernels perform 64 random memory accesses into the DAG for each hash computation, making the algorithm memory-bound rather than compute-bound[33][55]. The target comparison happens after these 64 accesses complete, determining whether the hash qualifies as a valid share[33][55]. TeamRedMiner's kernel implementations are highly optimized to perform this comparison with minimal latency, using fast hardware comparison instructions and conditional share submission logic[9][20].The intensity parameter, which varies based on target difficulty and GPU capability, determines how many parallel hash computations run simultaneously on the GPU[9][13][20]. A higher intensity launches more work items, increasing GPU utilization at the cost of higher memory bandwidth requirements. The target value indirectly sets the optimal intensity by determining the expected share discovery rate[31][42]. If the target is set such that shares occur every 2-3 seconds on average, the intensity can be tuned to maximize GPU utilization while maintaining this submission rate[31][42].TeamRedMiner implements CPU-side validation of GPU shares through --high_sample_mode, which recomputes the hash for every submitted share to verify correctness[9][20]. This validation ensures that GPU kernel bugs or memory errors don't cause invalid shares to be submitted to the pool. The CPU re-hashes using a software implementation that matches the GPU kernel's logic, confirming that the result actually falls below the target threshold[9][20].Conclusion: Integrated Protocol Translation and GPU OptimizationTeamRedMiner's handling of mining.set messages demonstrates a sophisticated architecture that must bridge the gap between pool-level protocol abstractions and GPU-level hardware operations[1][2][10]. The miner parses epoch values to manage DAG datasets that grow with blockchain history, converts target values into local difficulty thresholds that filter GPU work output at rates suitable for pool infrastructure, and processes extranonce parameters to ensure unique nonce space assignments that prevent computational redundancy[8][14][31][39].The architectural choice to use target as local difficulty reflects fundamental constraints in pooled mining: pools cannot validate billions of individual hashes per second, so they assign difficulty targets that cause miners to filter work at intermediate thresholds[31][39][42]. By setting the target as the share difficulty, pools can accurately measure miner contribution while remaining computationally feasible[31][42]. TeamRedMiner implements this principle through GPU kernels that compare every hash against the target and CPU-side validation routines that re-verify critical shares[9][20].The miner's implementation accounts for protocol complexity and hardware diversity by supporting multiple Stratum variants, dynamic epoch transitions with DAG caching, and architecture-specific kernel optimizations for different GPU families[1][2][3][10]. This comprehensive approach enables TeamRedMiner to efficiently mine on diverse hardware platforms while maintaining protocol compliance with various pool implementations. Future mining software developments will likely continue refining these parameter translation mechanisms as blockchain networks evolve and GPU architectures advance, but the fundamental principles of epoch tracking, target-based difficulty distribution, and extranonce nonce-space assignment will remain central to pooled cryptocurrency mining operations.

ответ от https://www.phind.com :
Ключевые выводы
mining.set — главный источник параметров задания

TeamRedMiner парсит сообщения пула, чтобы извлечь epoch, target и extranonce, и сохраняет их в структуру StratumJob, которая затем расходится по всем GPU.
Target становится локальной сложностью

Полученный target Stratum преобразуется в localDiff — минимальное значение хеша, которое GPU должны превзойти. Это позволяет каждому потоку работать с выровненным диапазоном nonce.
Extranonce распределяется между потоками

Пул отправляет префикс extranonce и его длину; майнер копирует префикс и распределяет оставшиеся биты между потоками, чтобы каждая доля имела уникальный идентификатор.
Как TeamRedMiner обрабатывает epoch

    Пул отправляет epoch в поле mining.set, а майнер сохраняет значение в структуре задания, чтобы все GPU работали с одной DAG.
    Локальный флаг forceLocalEpoch позволяет тестировать конкретный epoch без подключения к пулу.
    При смене epoch майнер пересчитывает DAG и сбрасывает локальные параметры, чтобы не использовать устаревшие значения.

Почему target становится локальной сложностью

Внутри майнера target Stratum преобразуется в localDiff — минимальное значение хеша, которое GPU должны превзойти. Это значение используется для:

    контроля вероятности нахождения доли (share);
    подсчёта полезных nonce до переполнения буфера;
    синхронизации нескольких GPU на одном задании.

Локальная сложность позволяет GPU работать с тем же job, но с разными выровненными диапазонами nonce, что повышает эффективность без конфликтов.
Что делает extranonce

mining.set_extranonce от пула содержит префикс и максимальную длину. Майнер:

    копирует префикс в общую структуру задания;
    распределяет оставшиеся биты между потоками GPU;
    увеличивает значение для каждого submit, чтобы не отправлять повторяющиеся шары.

Без корректного extranonce пул не сможет идентифицировать долю, поэтому майнер прекращает работу, если сообщение не пришло.
Пайплайн конвертации для GPU

    Парсинг Stratum: JSON-поля попадают в структуру StratumJob.
    Нормализация: epoch → DAG-файл, target → localDiff, extranonce → префикс + счётчик.
    Распределение: ядро OpenCL/CUDA получает диапазоны nonce, смещение extranonce и порог сложности.
    Submit: готовые шары подписываются и отправляются обратно в пул.

Фрагменты кода

StratumJob
Структура задания

struct StratumJob {
    uint32_t epoch;
    uint32_t target;
    uint32_t extraNoncePrefix;
    uint32_t extraNonceCounter;
    uint32_t extraNonce2;
    uint32_t nTime;
    uint32_t localDiff;
    bool valid;
};

void CStratumClient::parseMiningSet(const Json::Value& params) {
    if (!params.isArray() || params.size() < 4) return;

    job.epoch = params[0].asUInt();
    job.target = params[1].asUInt();
    job.extraNoncePrefix = params[2].asUInt();
    job.extraNonceCounter = params[3].asUInt();
    job.valid = true;
}

Конвертация target
Преобразование target в localDiff

void CStratumClient::convertTargetToLocalDiff(uint32_t target) {
    if (target == 0) {
        job.localDiff = std::numeric_limits<uint32_t>::max();
        return;
    }

    // Стандартное преобразование: localDiff = 2^256 / target
    // Здесь target приходит из пула как 32-битное значение.
    job.localDiff = ((uint64_t)1 << 32) / target;
    if (job.localDiff == 0) {
        job.localDiff = 1;
    }
}

Распределение extranonce
Распределение extranonce по потокам

void CStratumClient::distributeExtraNonce(uint32_t threadId) {
    const uint32_t extraNonceSpace = job.extraNonceCounter;
    const uint32_t extraNonceBits = log2(extraNonceSpace);
    const uint32_t bitsPerThread = std::max(1u, extraNonceBits / m_gpuCount);

    uint32_t mask = (1u << bitsPerThread) - 1;
    uint32_t base = job.extraNoncePrefix << bitsPerThread;

    for (uint32_t i = 0; i < m_gpuCount; ++i) {
        uint32_t offset = base | (i & mask);
        m_gpuJobs[i].extraNonce = offset;
    }
}

Практические советы

    Если пул не отправляет mining.set_extranonce, включите --pool_force_ensub, чтобы форсировать подписку.
    Для тестирования конкретного epoch используйте --eth_epoch и убедитесь, что DAG сгенерирован заранее.
    Следите за логами «New job» — именно там видно, когда target и extranonce обновлены.