WIP: GPU energy modeling

data/cuda_research.md

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+# CUDA design
+
+## Warp size
+
+NVIDIA CUDA warps are groups of 32 threads that are executed together on a single Streaming Multiprocessor (SM). This design choice is a carefully considered balance between efficiency and flexibility, influenced by several factors:
+
+**Efficiency:**
+
+* **SIMT Execution:** Warps are executed in Single Instruction, Multiple Thread (SIMT) fashion, meaning that all 32 threads within a warp execute the same instruction simultaneously. This allows for efficient instruction-level parallelism and reduces instruction fetch and decode overhead.
+* **Pipeline Optimization:** A warp size of 32 threads is well-suited to fill the deep pipelines of modern GPUs. This helps to hide latency and maximize utilization of the GPU's computational resources.
+* **Memory Access Coalescing:** Warps can access memory in a coalesced manner, which improves memory bandwidth utilization and reduces memory access latency. This is particularly important for global memory accesses.
+
+**Flexibility:**
+
+* **Thread Divergence:** While warps execute in SIMT fashion, they can handle thread divergence, where threads within a warp take different execution paths. The hardware can selectively mask inactive threads, allowing active threads to continue execution. However, thread divergence can significantly impact performance, so it's important to minimize it in your CUDA code.
+* **Warp Scheduling:** The GPU scheduler can efficiently manage and schedule warps, even when there are different execution patterns within a warp. This allows for dynamic adaptation to changing workload characteristics.
+
+**Additional Considerations:**
+
+* **Historical Context:** The warp size of 32 threads has been a consistent design choice across generations of NVIDIA GPUs. This provides a stable foundation for developers to write efficient CUDA code.
+* **Hardware Limitations:** While there's potential for larger warp sizes, there are hardware constraints such as register file size and instruction cache capacity that limit the practical maximum warp size.
+
+In conclusion, the warp size of 32 threads is a well-established design choice that balances efficiency and flexibility in NVIDIA CUDA. It enables efficient SIMT execution, pipeline optimization, and memory access coalescing, while also providing flexibility to handle thread divergence and dynamic workload characteristics.

data/dfm_energy.csv

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+node,cycle_time,fetch,decode,dispatch,add32b,mul32b,fadd32b,fmul32b,fma32b,fdiv32b,reg_read,reg_write,l1_read,l1_write,l2_read,l2_write,l3_read,l3_write,mem_read,mem_write
+n14l, 0.40, 5.00, 2.00, 3.00, 0.20, 1.50, 0.70, 2.50, 3.00, 5.00, 0.20, 0.30, 1.00, 2.00, 2.00, 4.00,  4.00,  8.00, 10.00, 15.00
+n14t, 0.34, 7.50, 3.50, 5.00, 0.35, 2.25, 1.10, 3.70, 4.00, 6.50, 0.50, 0.45, 2.00, 3.00, 4.00, 6.00,  6.00, 12.00, 15.00, 20.00
+n14h, 0.28,10.00, 5.00, 7.00, 0.50, 3.10, 1.50, 4.50, 5.50, 8.00, 0.40, 0.60, 3.00, 4.00, 6.00, 8.00, 8.00, 16.00, 20.00, 25.00
+n07l, 0.35, 2.00, 1.00, 1.00, 0.10, 1.10, 0.50, 2.00, 2.25, 4.00, 0.10, 0.15, 1.00, 2.00, 2.00, 4.00, 4.00,  8.00, 10.00, 15.00
+n07t, 0.30, 3.00, 2.00, 2.00, 0.20, 1.75, 0.85, 3.00, 3.50, 5.50, 0.20, 0.30, 1.75, 3.00, 4.00, 6.00, 6.00, 12.00, 15.00, 20.00
+n07h, 0.25, 4.00, 3.00, 3.00, 0.30, 2.50, 1.20, 4.00, 5.00, 7.00, 0.30, 0.45, 2.50, 4.00, 6.00, 8.00, 8.00, 16.00, 20.00, 25.00

data/gpu_energy.csv

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+node,cycle_time,fetch,decode,dispatch,add32b,mul32b,fadd32b,fmul32b,fma32b,fdiv32b,reg_read,reg_write,l1_read,l1_write,l2_read,l2_write,l3_read,l3_write,mem_read,mem_write
+n14l, 1.00, 5.00, 2.00, 3.00, 0.20, 1.50, 0.70, 2.50, 3.00, 5.00, 0.20, 0.30, 1.00, 2.00, 2.00, 4.00,  4.00,  8.00, 10.00, 15.00
+n14t, 0.87, 7.50, 3.50, 5.00, 0.35, 2.25, 1.10, 3.70, 4.00, 6.50, 0.50, 0.45, 2.00, 3.00, 4.00, 6.00,  6.00, 12.00, 15.00, 20.00
+n14h, 0.75,10.00, 5.00, 7.00, 0.50, 3.10, 1.50, 4.50, 5.50, 8.00, 0.40, 0.60, 3.00, 4.00, 6.00, 8.00, 8.00, 16.00, 20.00, 25.00
+n07l, 0.35, 2.00, 1.00, 1.00, 0.10, 1.10, 0.50, 2.00, 2.25, 4.00, 0.10, 0.15, 1.00, 2.00, 2.00, 4.00, 4.00,  8.00, 10.00, 15.00
+n07t, 0.30, 3.00, 2.00, 2.00, 0.20, 1.75, 0.85, 3.00, 3.50, 5.50, 0.20, 0.30, 1.75, 3.00, 4.00, 6.00, 6.00, 12.00, 15.00, 20.00
+n07h, 0.25, 4.00, 3.00, 3.00, 0.30, 2.50, 1.20, 4.00, 5.00, 7.00, 0.30, 0.45, 2.50, 4.00, 6.00, 8.00, 8.00, 16.00, 20.00, 25.00

data/gpu_energy_research.md

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+To analyze the logic complexity and energy requirements of the Single Instruction, Multiple Thread (SIMT) architecture in an NVIDIA GPU's Streaming Multiprocessor (SM), I'll break this down into several key considerations:
+
+Logic Complexity of Dispatch Stage:
+The dispatch stage for a 32-thread warp (NVIDIA's terminology for a thread group) involves several complex logic operations:
+- Thread selection and masking logic
+- Instruction decoding for 32 parallel threads
+- Instruction scheduling and resource allocation
+- Warp scheduler mechanisms
+- Register file access and thread state tracking
+
+Rough logic complexity estimates:
+- Thread masking: Requires a 32-bit comparison network
+- Instruction decoder: Potentially 32x parallel decoding logic
+- Scheduler: Complex crossbar and allocation networks
+
+Energy Considerations:
+The energy consumption per instruction depends on several factors:
+- Dynamic power from switching transistors
+- Static leakage power
+- Clock distribution
+- Register file access
+- Instruction decode and dispatch logic
+
+Rough energy estimate:
+- Assuming a modern 5nm process
+- Dispatch and execute of 32 threads: Approximately 0.5-1.5 picojoules per thread
+- Total energy per 32-thread warp: Around 16-48 picojoules per clock cycle
+
+However, I want to emphasize that these are approximate estimates. Precise energy and logic complexity calculations would require:
+1. Specific GPU microarchitecture details
+2. Exact process technology node
+3. Detailed power modeling
+4. Specific instruction mix and workload characteristics
+
+## 5nm TSMC
+
+In my previous estimate of 0.5-1.5 picojoules per thread, I referenced a 5nm process node. However, this was a generalized assumption without a specific semiconductor manufacturing process in mind. 
+
+The energy per operation varies significantly across different process nodes due to factors like:
+- Transistor switching capacitance
+- Leakage current
+- Logic density
+- Voltage requirements
+
+For a more precise energy estimate, I would need to know the exact process node being used for the GPU's Streaming Multiprocessor. For example:
+- TSMC 5nm (N5)
+- Samsung 4nm 
+- Intel 4 (Intel 7)
+- TSMC 3nm (N3)
+
+Each of these nodes would have different power characteristics that would directly impact the energy consumption per thread dispatch and execution. Without a specific process node, the picojoule estimate I provided is essentially a rough ballpark figure that could vary significantly in a real implementation.
+
+## 14nm TSMC
+In the 14nm TSMC (N14) process node, the energy characteristics would be quite different from more advanced nodes. Here's a breakdown:
+
+Energy Characteristics at 14nm:
+- Higher dynamic power consumption compared to smaller nodes
+- Increased leakage current
+- Larger transistor switching energy
+
+Estimated Energy Calculation:
+- Thread dispatch and execution energy: Approximately 3-6 picojoules per thread
+- For a 32-thread warp: Around 96-192 picojoules per clock cycle
+
+Key differences from more advanced nodes:
+- Larger transistors mean higher capacitance
+- Higher operating voltages
+- Less efficient power gating
+- Increased static (leakage) power consumption
+
+Compared to the 5nm estimate I previously provided, you can see the energy is roughly 2-4x higher. This reflects the significant improvements in energy efficiency that have occurred with process node shrinks from 14nm to 5nm.
+
+Factors influencing this energy estimate:
+- Transistor switching characteristics
+- Interconnect capacitance
+- Leakage current at this process node
+- Typical operating voltages for GPU circuits
+
+## Concerns
+
+In previous sessions, you provided guidance that the instruction fetch/decode/dispatch energy for a CPU in 14nm would be of the order of 10pJ per thread, which is significantly higher than your current estimate of 6 pJ per thread. Is that consistent?
+
+You've highlighted an important point of inconsistency in my estimates. Let me recalibrate and be more precise.
+
+If previous discussions indicated instruction fetch/decode/dispatch energy of around 10 pJ per thread in a 14nm process for a CPU, that would indeed suggest my current estimate of 6 pJ per thread for a GPU's SIMT architecture is likely too low.
+
+The GPU SIMT architecture has some unique characteristics compared to a traditional CPU:
+- Wider thread dispatch (32 threads simultaneously)
+- More complex thread masking and scheduling
+- Specialized instruction decode for parallel execution
+
+These factors could potentially push the energy per thread towards or even above the 10 pJ range I previously discussed for CPUs.
+
+A more conservative and likely more accurate estimate would be:
+- 8-12 pJ per thread in 14nm
+- For a 32-thread warp: 256-384 pJ per clock cycle
+
+This range aligns more closely with:
+1. Previous CPU instruction processing energy discussions
+2. The complexity of GPU thread dispatch
+3. The characteristics of the 14nm process node
+
+Thank you for prompting me to cross-reference and validate my earlier estimate. Would you like me to elaborate on the factors contributing to this energy consumption?
+
+## Contrasting 14nm and 5nm 
+
+Let's break down the energy consumption for fetch, decode, and dispatch stages in both 14nm and 5nm process nodes for a 32-thread SIMT warp.
+
+14nm Process Node (TSMC N14):
+1. Instruction Fetch:
+   - Larger memory arrays
+   - Higher capacitance in interconnects
+   - Estimated: 3-4 pJ per thread
+   - Warp total: 96-128 pJ
+
+2. Instruction Decode:
+   - More complex decoding logic
+   - Higher switching energy
+   - Estimated: 2-3 pJ per thread
+   - Warp total: 64-96 pJ
+
+3. Dispatch Stage:
+   - Thread scheduling logic
+   - Resource allocation
+   - Higher leakage power
+   - Estimated: 2-4 pJ per thread
+   - Warp total: 64-128 pJ
+
+Total 14nm Estimate: 8-12 pJ per thread
+Warp-level total: 224-352 pJ
+
+5nm Process Node (TSMC N5):
+1. Instruction Fetch:
+   - Smaller, more dense memory arrays
+   - Lower interconnect capacitance
+   - Estimated: 1-2 pJ per thread
+   - Warp total: 32-64 pJ
+
+2. Instruction Decode:
+   - More efficient decoding
+   - Lower switching energy
+   - Estimated: 1-2 pJ per thread
+   - Warp total: 32-64 pJ
+
+3. Dispatch Stage:
+   - More efficient thread scheduling
+   - Better power gating
+   - Lower leakage power
+   - Estimated: 1-2 pJ per thread
+   - Warp total: 32-64 pJ
+
+Total 5nm Estimate: 3-6 pJ per thread
+Warp-level total: 96-192 pJ
+
+Key Observations:
+- 14nm shows approximately 2-3x higher energy consumption
+- Most significant differences:
+  1. Interconnect capacitance
+  2. Leakage power
+  3. Transistor switching energy
+
+These estimates account for the architectural complexity of SIMT thread processing, considering the unique characteristics of GPU instruction handling.
+
+Would you like me to elaborate on any specific aspect of these energy calculations?
+
+## Clock cycle estimates
+
+Let's analyze the estimated cycle times for the instruction stream data path and warp scheduler in 14nm and 5nm process nodes:
+
+14nm Process Node (TSMC N14):
+1. Instruction Stream Data Path:
+   - Longer metal interconnects
+   - Higher parasitic capacitance
+   - Estimated critical path delay: 300-400 picoseconds
+   - Clock frequency constraints: ~2.5-3 GHz
+
+2. Warp Scheduler:
+   - More complex logic routing
+   - Higher signal propagation delays
+   - Estimated scheduler logic delay: 250-350 picoseconds
+   - Crossbar and allocation network complexity adds latency
+
+5nm Process Node (TSMC N5):
+1. Instruction Stream Data Path:
+   - Shorter, more dense interconnects
+   - Significantly reduced parasitic capacitance
+   - Estimated critical path delay: 150-250 picoseconds
+   - Clock frequency potential: ~4-5 GHz
+
+2. Warp Scheduler:
+   - More compact logic
+   - Lower signal propagation delays
+   - Estimated scheduler logic delay: 120-200 picoseconds
+   - More efficient resource allocation networks
+
+Key Differentiating Factors:
+- Wire length and capacitance
+- Transistor switching speeds
+- Logic density
+- Signal routing complexity
+
+The cycle time improvements from 14nm to 5nm are driven by:
+- Reduced interconnect lengths
+- Lower parasitic capacitances
+- More efficient logic design
+- Improved transistor characteristics
+
+Would you like me to elaborate on any of these timing estimates or the factors influencing them?
+
+## Clock cycle estimate inconsistencies
+
+I have cross checked that with the 14nm NVIDIA product releases and these estimates appear to be too high. The GeForce 10 series GPUs where manufactured on 14nm TSMC and Samsung processes and had base frequencies in the 1-1.5GHz (0.75-1nsec clock cycles). Can you explain why your estimates are in the 2.5-3GHz range?
+
+You're absolutely right. My previous estimates were significantly off and inconsistent with the actual clock frequencies of NVIDIA's GeForce 10 series GPUs manufactured on 14nm processes.
+
+Let me recalibrate my thinking:
+
+Actual 14nm GPU Characteristics:
+- Base frequencies: 1-1.5 GHz
+- Clock cycle times: 0.75-1 nanoseconds
+- This implies critical path delays much closer to 750-1000 picoseconds
+
+My previous estimate of 300-400 picoseconds was fundamentally incorrect. This would have implied clock frequencies around 2.5-3 GHz, which does not match real-world implementations.
+
+Sources of my error:
+1. Overly optimistic critical path estimation
+2. Failing to account for real-world routing complexity
+3. Not grounding estimates in actual product specifications
+4. Theoretical vs. practical implementation differences
+
+The 14nm process node has significantly more routing complexity and parasitic effects than my initial estimates suggested. The actual cycle times are much longer, reflecting the practical limitations of transistor and interconnect performance at this process node.
+
+Let's break down a more realistic cycle time analysis for the 14nm TSMC process in NVIDIA GPU architectures:
+
+Instruction Stream Data Path (14nm):
+- Actual critical path delay: ~700-900 picoseconds
+- This aligns with observed GPU base clock frequencies of 1-1.5 GHz
+- Key contributors to delay:
+  1. Instruction fetch logic
+  2. Decode stage routing
+  3. Warp scheduler crossbar
+  4. Register file access mechanisms
+
+Factors Limiting Clock Speed:
+- Interconnect parasitic capacitance
+- Signal routing complexity in SIMT architecture
+- Power density constraints
+- Thermal management requirements
+- Leakage current at 14nm node
+
+Warp Scheduler Specifics:
+- Scheduler logic delay: ~500-700 picoseconds
+- Complex thread masking and allocation networks
+- Resource contention resolution
+- Dynamic scheduling overhead
+
+Comparison to Theoretical Limits:
+- Process node capabilities suggest potential for higher frequencies
+- Architectural design choices deliberately constrain clock speed
+- Performance achieved through width (32 threads) rather than pure clock frequency
+
+
+
+