SIMT vs SIMD: Parallelism in Modern Processors
Understanding SIMT
At first glance, the term SIMT (Single Instruction, Multiple Threads) might seem like a misnomer. How can a single instruction be shared across multiple threads? After all, threads are typically thought of as independent execution units, each with its own program counter, running different instructions or at least different data. So, how can they all execute the same instruction simultaneously?
The answer lies in the concept of a thread as a software construct. In a SIMT model, threads don’t necessarily have to be performing the exact same operations on the same data at all times. Rather, they simply need to have their program counters (or instruction pointers, for those familiar with Intel terminology) aligned during the execution of a particular instruction. In other words, as long as two threads are pointing to the same instruction, they can "share" that instruction, even if they might diverge later on. This synchronization of program counters across threads is all that’s required for multiple threads to execute a single instruction at the same time.
This model is what makes SIMT such a powerful and efficient way to scale parallel processing: each thread can operate independently but follow the same sequence of instructions, simplifying both hardware and software design.
In the world of parallel computing, SIMT (Single-Instruction Multiple Threads) and SIMD (Single Instruction Multiple Data) are two common approaches that power modern processors, particularly in the realm of GPUs. While both techniques involve executing the same operation across multiple threads or data elements simultaneously, they have key differences that make SIMT much easier to work with from a software development perspective.
In this article, I want to point out that even though most people think of SIMD when they think of graphics processors, GPUs are usually implemented with SIMT, and SIMD and SIMT are not really the same at all.
What is SIMD?
SIMD stands for Single Instruction Multiple Data, and it’s a technique that allows a single processor to perform the same operation on multiple pieces of data at once. Think of it as a vector processor that can perform operations like adding or multiplying on entire arrays of numbers in parallel, all with a single instruction.
SIMD requires the programmer to explicitly manage how data is chunked and aligned to fit the vector size of the processor. For instance, if the processor can handle vectors of 4 or 8 elements at a time, the programmer must manually ensure the data is structured in such a way that the operations can be efficiently executed. This often involves padding data or reorganizing it to fit the processing model, which can add significant complexity to the software design.
What is SIMT?
SIMT, on the other hand, stands for Single-Instruction Multiple Threads. This model is more commonly found in GPU architectures like those from NVIDIA, where hundreds or thousands of threads can be executed simultaneously. Each thread is essentially an independent entity, but they all execute the same instruction concurrently—hence "single instruction" across "multiple threads."
One of the most important features of SIMT is that each thread behaves much like a CPU, with its own program counter and execution context. This means that the programmer doesn’t need to worry about chunking or vectorizing data manually. Instead, each thread is free to operate on a different piece of data, and the hardware takes care of the parallelism. You can think of a SIMT system as an array of small processors, all performing the same task simultaneously but potentially on different pieces of data.
Why SIMT is Easier to Deal With
The key advantage of SIMT from a software development perspective lies in its simplicity and flexibility. Here’s why:
Threads Are Like CPUs: In a SIMT model, each thread is effectively a small CPU that can execute its own instructions independently. This makes it easier to program, because each thread can handle its own piece of data, much like how we think of multi-threading on CPUs. You don’t need to worry about whether your data fits into a specific vector size; instead, you simply think of how you would program an individual scalar CPU on one chunk of the data, and then scale that thinking to a big array of small processors. It’s a bit like “proof by induction“ for processors.
No Need to Manually Chunk Data: Unlike SIMD, where you must manually chunk your data into vectors of the correct size, SIMT lets you program individual threads independently. Each thread is free to work on a different piece of data, and there’s no need for explicit management of data chunking or alignment. This reduces the cognitive load on the programmer and makes the code easier to understand and maintain.
Flexible Scalability: With SIMT, the number of threads can be scaled up or down easily, depending on the workload and the available hardware resources. You don’t have to worry about how your data aligns with a vector or processor; the hardware abstracts away this complexity, allowing you to focus on the logic of the application.
One of the powerful features of the SIMT model is its ability to handle branch divergence through a technique called predication. In traditional parallel models, when threads encounter a conditional branch, some threads may follow one path of execution while others follow a different path, causing divergence. This divergence often leads to inefficiencies, as different threads may need to execute different instructions, which is particularly problematic in SIMD systems where all threads must remain in lockstep.
However, in SIMT, the situation is different. Since each thread is treated like a small, independent processor, it can handle branch divergence more gracefully. When a conditional branch occurs, the hardware can "predicate" the execution of instructions, allowing threads to either execute or skip particular instructions based on their individual condition without halting the entire thread group. In essence, each thread evaluates its own condition and, if necessary, executes different instructions or skips them entirely, all while keeping the other threads on their respective paths. This approach allows SIMT to maintain high parallelism even in the presence of divergent branches, avoiding the inefficiency of having all threads execute the same instructions regardless of their individual conditions.
Predication helps mitigate the performance cost of branch divergence, especially in workloads where conditional branches are common. By enabling threads to execute independently without waiting for others to catch up, SIMT can keep threads running in parallel, even when they take different execution paths, further enhancing the flexibility and scalability of the model.
Introducing this flexibility doesn’t come for free. It introduces new challenges. Specifically, keeping threads synchronized with each other is required for peak performance. As noted above, branch divergence is supported but predication is a pretty low-efficiency implementation of it. Writing SIMT code just like you would write single-threaded code can cause your system to perform really poorly. There are techniques to keep your code “Bulk Synchronous” and that kind of programming model is what CUDA is designed to take advantage of. I’m not going to go too much into detail on the performance/area implications of building SIMT units instead of SIMD units, but I will say that because of the increased complexity, SIMT is more area intensive - it allows each set of threads to share decoder infrastructure, as opposed to a multicore CPU, but it requires duplication of register files and ALUs.
In summary, SIMT is like spider-man. “With great power (of lots of ALUs and threads running in parallel) comes great responsibility (to efficiently utilize all those resources through bulk-synchronous programming).
SIMD: The Challenge of Explicit Vectorization
In contrast, SIMD requires the programmer to be much more explicit. Data must be chunked into chunks that are the right size for the processor’s vector capabilities. If you’re dealing with a processor that handles vectors of 4, 8, or 16 elements at once, you need to carefully partition your data to fit these boundaries.
For example, if you have an array of 100 elements but your processor handles vectors of 8, you’ll need to manage how to split that array into 8-element chunks. This can lead to inefficiencies if your data size isn’t perfectly divisible by the vector size, requiring extra code to handle the remainder or to pad the data.
Furthermore, SIMD typically requires careful optimization to avoid performance pitfalls. The programmer has to ensure that the operations are optimized for the underlying hardware, often involving low-level techniques like loop unrolling, data alignment, and prefetching. This makes SIMD more difficult to program and less flexible than SIMT.
Conclusion: SIMT for Simplicity, SIMD for Area Efficiency
In summary, while both SIMT and SIMD are powerful techniques for parallelism, SIMT offers a much more intuitive and flexible approach for software development. It allows programmers to think of each thread as an independent unit, much like a CPU, which simplifies the development process. In contrast, SIMD requires the programmer to carefully manage how data is structured to fit the hardware’s vector size, adding complexity to the programming model.
SIMD doesn’t necessarily offer better control over execution; rather, it provides better peak performance in terms of power efficiency and area efficiency, as it reduces hardware overhead by using fewer execution units for more data. While this can lead to performance benefits in certain contexts, it requires more careful manual management of data and optimization to achieve the best results.
An important advantage of SIMT is the ability to scale thread-level parallelism in a highly flexible way. Having many threads is inherently beneficial for two key reasons:
High Memory Bandwidth Consumption: SIMT systems can easily exploit high memory bandwidth, allowing the processors to use all of their computational resources effectively, rather than being limited by memory access.
Latency Tolerance: Because there are many threads running simultaneously, SIMT systems can tolerate latency more effectively. If one thread encounters a delay (e.g., due to memory access), other threads can continue execution, improving overall throughput and reducing idle time.
SIMD, by contrast, suffers from the "lockstep" nature of its processing model. All operations in a vector must be completed together, meaning if any data or instruction causes a delay, all threads in that vector are held up. This makes it less tolerant to latency and less efficient in systems requiring dynamic adaptability.
Ultimately, the choice between SIMT and SIMD depends on the specific needs of the application. SIMT is generally easier to program, more scalable, and offers better performance in latency-tolerant, memory-bound workloads, making it a better fit for most applications, especially those leveraging GPUs. SIMD, however, may be advantageous in cases where performance per unit of hardware is a critical concern, but it doesn’t provide the same scalability and flexibility as SIMT.
I'm excited to share more posts in the future that dive deeper into how these processors are built, and explore why concepts like latency tolerance naturally emerge from the SIMT paradigm.
