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Now I was confused, I needed context as I have experienced something similar myself.
I had 2 notebooks, both with similar processors, ram memory and graphics, I installed windows 7 32 bit on both, one was very fast, the other very slow, then I installed windows 7 64 bit on both, the one that was fast on 32 bit, now for some reason was much slower on 64 bit, much much slower, while the one that was slow on 32 bit, now flew on 64 bit. And it is not the first time that happened to me, in fact I started to test irrlicht, and the one that was 32 bits faster, ran much more fluid irrlicht, more than 800 FPS, while the one that ran faster 64 bits, irrlicht maximum was 140 FPS. The one running at 800 FPS was struggling with the software renderer, it was barely holding at about 20 FPS, the one running at 140 FPS was running very smoothly with the software renderer, 60 FPS or more.
So it is clear that no matter what software you use, the result will vary depending on the hardware, even if they have similar technical data, their internal functionality can vary a lot.
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Well let's stop that, and focus on the deepseek r1 vs chatgpt o3-mini discussion with reasoning.
I asked them both the same question, first prompt: in c++98, I have read that it is faster to work with small valuesTo store the result, what is faster for the cpu? To use the architecture value as 32 bits or 64 bits, or to store the value in a smaller value?
deepseekr1 wrote:I mean, depending on how the result is stored, which is faster when referencing the memory, which is faster when wanting to access that result, as far as the physical operation of the CPU is concerned.
Let's break down your technical question into parts to better understand the process:
Processing data smaller than the CPU bus size 2.
On a 64-bit CPU, the processor does read and process data smaller than its native size (e.g., 8, 16, or 32 bits), but there is not always an explicit or expensive "conversion". This depends on how memory is accessed and what instructions the CPU executes.
Memory access: Modern CPUs are designed to access memory in aligned blocks (usually the size of the bus, such as 64 bits), but can extract/modify sub-blocks (e.g. 1 byte) by bit masking/shifting operations.
CPU registers: When a small value (e.g. 8 bits) is loaded into a 64-bit register, the CPU can:
Extend with zeros (for unsigned data).
Extend with the sign bit (for signed data, such as int8_t to int64_t).
This is done with specific instructions (e.g. MOVZX or MOVSX on x86), which are very fast (1 clock cycle or less).
In electronics, any change of state (e.g., a bit going from 0 to 1) consumes power and generates heat due to transistor switching. However:
Extension (zero/sign) operations are dedicated instructions in the CPU, implemented at the hardware level with specific circuitry. These operations are not "computations" in the algorithmic sense, but direct physical actions (e.g. activating electrical wires to propagate the sign bit). Their energy cost is negligible compared to complex operations such as multiplications or RAM accesses.
Example: Extending an int8_t to 64 bits with MOVSX on x86 consumes ~0.3-1 picojoules (depending on the CPU manufacturing process), while a 64-bit multiplication operation can consume ~10-100 times more.
Heat in a CPU is dominated by two factors:
Clock frequency: higher frequency, more switches per second → more heat.
Number of active transistors: complex operations (e.g. AVX, floating divisions) activate more circuits.
Small type conversions are not relevant in this context:
If you use uint8_t instead of uint64_t, even if the CPU does extensions, the savings in memory/cache accesses reduces overall CPU activity (fewer cycles waiting for data from RAM). This mitigates heat in applications with large volumes of data.
Modern systems have a key factor:
Dennard's Law (end of scalability): Since ~2005, reducing the size of transistors no longer proportionally reduces their power consumption. Modern CPUs are more densely packed, which increases heat density.
If your priority is to minimize CPU electrical stress:
Avoid operations with floating numbers: They consume more power than integer extensions.
Use SIMD instructions: Processing multiple small data in parallel (e.g., 16 uint8_t in a 128-bit register) reduces the number of individual operations.
Disable Hyper-Threading/SMT: Fewer simultaneous threads → fewer chaotic switches.
Conclusion
Small type conversions are not a significant factor. The energy "weight" of these operations is negligible compared to other activities such as RAM accesses, float operations, or the use of vector units (AVX/SSE).
Optimizing memory usage with small types is still a good practice, but if heat is your central concern, focus on:
Improving system cooling.
Reduce algorithmic complexity (e.g., replace nested loops with vectorized operations).
chatgpto3mini wrote:In terms of optimizing memory access and CPU performance, several factors come into play depending on data size and CPU architecture.
Use of small vs. large values (32-bit vs. 64-bit):
Architecture word size: modern CPUs are generally optimized to work with the architecture word size, which is typically 64 bits on modern systems. This means that operating on 64-bit values can be faster on 64-bit architectures, as the CPU is designed to handle 64 bits per clock cycle, which avoids the need to split data into multiple parts or perform additional operations.
Memory accessibility: In terms of memory access, modern processors can generally access data that matches the word size of the architecture more efficiently. Accessing a 64-bit value on a 64-bit architecture will generally be faster than accessing two 32-bit values or smaller values, because the CPU does not have to do more load/store operations and can take advantage of larger memory bandwidths.
Accessibility and efficiency of smaller values:
Smaller size: Using smaller values such as 8 or 16 bits could make memory more efficiently utilized, especially in cache- or memory-constrained systems. However, while this may save space, it may also cause the CPU to perform additional operations to adjust the data size during operations. In addition, modern CPUs are generally not as optimized to efficiently perform operations on data sizes smaller than the architecture's word size (64-bit).
Memory fragmentation: In some situations, using smaller values could result in memory fragmentation, which can degrade CPU performance if data is not well organized in cache or main memory.
Cache and memory alignment:
Data alignment: Modern CPUs are typically faster when accessing data that is aligned to the architecture's word size (e.g., in 64-bit blocks for a 64-bit architecture). If you store a 64-bit value in a 32-bit or 16-bit variable, this can cause a performance penalty because the CPU may have to make adjustments to access memory, which increases access latency.
Practical conclusion:
In general, using values that match the word size of the architecture (e.g., 64 bits on a 64-bit processor) is fastest, as it allows the CPU to handle the data more efficiently, taking advantage of the architecture optimization.
Using smaller types can be useful in memory constrained situations or when space optimization is critical, but usually comes with performance penalties due to the overhead of performing additional operations and possible memory misalignment.
In summary, to maximize memory access speed and CPU performance, it is more advisable to store results in a size that matches the CPU architecture (e.g., 64 bits on a 64-bit CPU).
deepseekr1 wrote:I understand your point and respect the depth of your analysis. Let's unravel this without fallacies, from semiconductor physics and CPU design.
1. Do calculations on a 64-bit CPU always use 64 bits?
No. Modern CPUs have registers and execution units of multiple sizes. For example, in x86-64:
64-bit registers: RAX, RBX, etc.
Modes of operation: You can use 8-bit, 16-bit, or 32-bit sub-registers (e.g., AL, AX, EAX).
Execution units: There are dedicated circuits for 8-bit, 16-bit, 32-bit, and 64-bit operations.
When you execute an operation with a uint8_t, the CPU does not process 64 bits. It turns on only the relevant transistors for the lower 8 bits of the register. The upper bits (56 bits) remain in a "no-switching" state (no power consumption).
Example: ADD AL, BL (sum of 8 bits) only activates the logic gates associated with 8 bits, not the entire 64-bit register.
2. Do small → large conversions involve "double trip" of current?
No. Electricity does not physically "travel back and forth" in an integrated circuit. Transistors switch states (on/off) through local electric fields (no electron movement as in a wire).
When you extend an int8_t to 64 bits:
Signed extension (MOVSX): bit 7 (sign) is replicated to bits 8-63 by a single voltage propagation step (not 56 separate operations). This is done with dedicated circuits (e.g., bit-repeat buffers), which operate in parallel and consume power equivalent to turning on a single transistor for all upper bits.
Unsigned extension (MOVZX): The upper bits are set to 0, which does not require switching (they are already at 0 by default in many designs).
There is no "double trip", just parallel electrical propagation (nanometers apart in silicon).
3. Heat and conductivity: Why is it not relevant for conversions?
The heat in a CPU depends on:
Number of transistors switching per cycle.
Switching frequency (GHz).
Applied voltage (Joule's Law: P=V2⋅f⋅CP=V2⋅f⋅C).
Extension operations (MOVSX, MOVZX) are not significant in this context:
Energy per operation: ~0.3-1 pJ versus ~100 pJ of an L3 cache operation or ~500 pJ of a RAM access.
Thermal impact: if you have a loop running 1 million 8→64 bit extensions, the consumption would be ~1 microjoule (negligible vs. ~100-500 joules of a modern game in 1 second).
4. Small data vs. big data: Which to choose?
If your priority is to minimize CPU electrical activity (not RAM):
Use the smallest data type that meets the necessary range.
Example: if you work with values from 0-255, use uint8_t. Fewer bits → fewer transistors switching in arithmetic operations.
Avoid unnecessary promotions:
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uint8_t a = 100, b = 200;
uint16_t c = a + b; // Better than promoting to 64 bits.
Use SIMD to process small data blocks:
Example: process 16 uint8_t values in a single 128-bit AVX2 register.
5. Numerical example: Real thermal impact
Suppose you have two scenarios:
Case A: You process an array of 1 million uint64_t.
Case B: You process an array of 1 million uint8_t (with 64-bit extensions).
Metric Case A (uint64_t) Case B (uint8_t + extensions)
RAM memory 8 MB 1 MB
Cache accesses More frequent Less frequent
Active transistors 64 bits × 1M 8 bits × 1M + 56 bits × 1M
Power consumed ~800 µJ ~150 µJ (operations + RAM)
Result: Case B generates less heat because it reduces the use of RAM and cache, which are the biggest energy consumers.
Conclusion
Small type conversions are not thermally relevant: Their energy impact is negligible compared to operations such as memory accesses or floating computations.
Final recommendation: Use small types to optimize memory and cache. Physics does not lie, but the devil is in the details: it is not the conversions, but the overall system architecture that defines heat and stability.
chatgpto3-mini wrote:You are right that modern processors perform internal optimizations when it comes to computations, and one of the techniques is to work with smaller size registers and then perform conversion to a larger size when necessary. This type of optimization occurs in several contexts, especially when the processor has specific instructions for working with 32-bit, 16-bit, or even 8-bit integers.
I will explain it in more detail:
1. Internal Computations in the CPU
Modern CPUs (both 32-bit and 64-bit) usually have an arithmetic-logic unit (ALU) that can handle calculations on different data sizes. If a processor has support for 32-bit, 16-bit, or even 8-bit integer operations, it can perform calculations internally on these smaller sizes without the need for automatic 64-bit conversions. However, what happens is the following:
32-bit operations on a 64-bit CPU: The processor can perform 32-bit arithmetic operations without the need to scale those values to 64 bits. The result of those 32-bit operations can remain in 32-bit registers.
Accumulation of results in 64-bit registers: If the computation requires a larger result (as in multiplication of two 32-bit values), the processor can accumulate the result in 64-bit registers to avoid overflow, but during intermediate operations, it can use smaller registers.
2. Optimization of Registers and CPU Operations
The use of 32-bit registers for intermediate calculations is common, and often the results of these calculations are stored in smaller registers if the final value does not need to be as large as 64 bits. Processors are designed to handle operations efficiently with these smaller data sizes, but when the values exceed the size of the intermediate registers (for example, during a multiplication or addition of large numbers), the final result will be adjusted to the necessary size (64 bits on a 64-bit CPU).
It is not necessary to store everything in 64 bits: If a calculation does not produce values that need more than 32 bits, the processor can perform internal operations with 32 bits and leave the result in a 32-bit value. However, if at any point the computed value exceeds 32 bits, then 64-bit storage will be required.
3. Alignment and Memory Access
While CPUs can handle internal calculations with smaller registers, the way memory is accessed also plays an important role. Accessing values that are not correctly aligned in memory or that do not match the architecture's word size can slow down performance, even if internal computations are performed with smaller register sizes.
4. Benefits of using smaller registers internally
Performance: Working with smaller registers can be faster, since smaller registers allow faster operations and take up less space within the CPU's internal architecture.
Memory efficiency: If you can use smaller registers and perform fast calculations, it can reduce the amount of memory you need to store data, which can be useful for operations where memory access is costly.
5. The role of 64-bit architecture
Although 64-bit CPUs are capable of handling 32-bit operations without a problem, it is more efficient that when the final computation requires 64 bits, it is performed directly with 64-bit registers to avoid conversions and time losses due to register overflow.
To summarize:
Internally, the CPU can work with smaller register sizes (such as 32 bits) without the need to convert to 64 bits for intermediate calculations. This can speed up operations if the values do not exceed the limits of those small registers.
Final storage: If the final result of a calculation is a large value, such as the product of two large integers, it is more efficient to store it in a register that matches the CPU architecture (e.g., 64 bits on a 64-bit CPU).
Memory access: Using smaller values may save memory, but can affect data alignment and CPU performance if not well optimized.
The key is how the processor handles computations and conversions between register sizes, and how data is aligned in memory to maximize bandwidth and memory access speed.
deepseekr1 wrote:Let's synthesize and clarify the technical dilemma, integrating both perspectives (yours, and what was discussed above):
Using small type vs. native (64-bit): key trade-offs 2.
Memory and cache
Small types (8/16/32 bits):
Advantage: reduce RAM/cache usage. Ex: A 1M int32_t array occupies 4 MB, vs. 8 MB with int64_t. This improves spatial locality and reduces cache misses.
Disadvantage: If data is not aligned (e.g., an int16_t at odd address), the CPU may need multiple memory accesses (~3-10 cycle penalty).
Native types (64-bit):
Advantage: inlined and atomic access on modern CPUs (1 cycle to load/store).
Disadvantage: Occupy double or quadruple memory, increasing cache misses on large datasets.
Arithmetic operations
Small types:
Advantage: Operations like ADD or MOV in 8/16/32 bits are just as fast as in 64 bits on x86-64 (e.g. ADD EAX, EBX vs ADD RAX, RBX).
Cons: If the result exceeds the size (ex: multiply two int32_t), promotion to 64 bits is required (extra operation).
Native types:
Advantage: Avoid extensions/promotions in intermediate computations.
Disadvantage: Small data operations waste memory bandwidth.
2. Which is faster for storing results?
It depends on the access context:
If the data is used immediately (ex: local variable in a loop):
Use the native type (64 bits). The CPU will work with 64-bit registers anyway, and you avoid extensions/truncations.
If the data is stored in a large array (e.g. batch processing):
Use the smallest type that covers the needed range (ex: uint8_t for 0-255). The cache savings offset the cost of point conversions.
Practical example
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// Case 1: Temporal variable in intensive calculation
int64_t result = value1 + value2; // Better 64 bits (avoids promotions).
// Case 2: Array of 10M elements
uint8_t buffer[10'000'000]; // Better 8 bits: 10 MB vs 80 MB (64 bits).
3. Myths and realities about conversions.
"Small→big conversions are slow":
Fact: Instructions like MOVZX (zero-extend) or MOVSX (sign-extend) take 1 cycle on modern CPUs. They are not a bottleneck.
"Using 64-bit is always faster":
Fact: Only true if the data exceeds 32 bits or atomic access is required. For small values, memory/cache cost dominates.
4. Practical recommendations in C++98
Prioritize small types in large arrays/structures:
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std::vector<uint32_t> data; // Better than int64_t if values fit in 32 bits.
Use native type for single heavy-use variables:
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int64_t accumulator = 0; // Avoid promotions in critical loops.
Explicit alignment:
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struct alignas(8) MiStruct { // Aligns to 64 bits (8 bytes).
uint16_t a, b, c;
};
Always profile:
Tools like gprof or valgrind --tool=cachegrind show you cache misses and CPU cycles.
5. Final conclusion.
To store results:
If the data is temporary and used in subsequent calculations: Use 64 bits.
If the data is part of a large collection: Use the smallest possible type.
The CPU is not the enemy:
Conversions are cheap, but cache misses are expensive. Optimize memory first, then operations.
In summary: There is no universal rule. The answer lies in the balance between memory usage and operation frequency, something you can only fine-tune with empirical measurements in your specific case.
