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where MBW is measured in Mflops/sec and BW stands for the available memory bandwidth in Mbytes/s, as measured by STREAM [11] benchmark. When any amount of data is accessed, with a minimum of one single byte, the entire 64-byte block that the data belongs to is actually transferred. Yes -- transistors do degrade over time and that means CPUs certainly do. Actually, bank 1 would be ready at t=50 nanosec. Q & A – Memory Benchmark This document provides some frequently asked questions about Sandra.Please read the Help File as well! MCDRAM is a very high bandwidth memory compared to DDR. Fig. Fig. When someone buys a RAM chip, the RAM will indicate it has a specific amount of memory, such as 10 GB. For the Trinity workloads, we see two behaviors: Cache unfriendly: Maximum performance is attained when the memory footprint is near or below the MCDRAM capacity and decreases dramatically when the problem size is larger. In our case, to saturate memory bandwidth we need at least 16,000 threads, for instance as 64 blocks of 256 threads each, where we observe a local peak. The processors are: 120 MHz IBM SP (P2SC “thin”, 128 KB L1), 250 MHz Origin 2000 (R10000, 32 KB L1, and 4 MB L2), 450 MHz T3E (DEC Alpha 21164, 8 KB L1, 96 KB unified L2), 400 MHz Pentium II (running Windows NT 4.0, 16 KB L1, and 512 KB L2), and 360 MHz SUN Ultra II (4 MB external cache). It’s less expensive for a thread to issue a read of four floats or four integers in one pass than to issue four individual reads. Not only is breaking up work into chunks and getting good alignment with the cache good for parallelization but these optimizations can also make a big difference to single-core performance. The plots in Figure 1.1 show the case in which each thread has only one outstanding memory request. In cache mode, memory accesses go through the MCDRAM cache. This makes the GPU model from Fermi onwards considerably easier to program than previous generations. In the GPU case we’re concerned primarily about the global memory bandwidth. [36] reduce effective latency in graph applications by using spare registers to store prefetched data. This measurement is not entirely accurate; it means the chip has a maximum memory bandwidth of 10 GB but will generally have a lower bandwidth. Thus, without careful consideration of how memory is used, you can easily receive a tiny fraction of the actual bandwidth available on the device. - RAM tests include: single/multi core bandwidth and latency. - Compare . If there are 32 ports in a router, the shared memory required is 32 × 2.5 Gbits = 80 Gbits, which would be impractical. Finally, we see that we can benefit even further from gauge compression, to reach our highest predicted intensity of 2.29 FLOP/byte when cache reuse, streaming stores and compression are all present. Our naive performance indicates that the problem is memory bandwidth bound, with an arithmetic intensity of around 0.92 FLOP/byte in single precision. MiniDFT without source code changes is set up to run ZGEMM best with one thread per core; 2 TPC and 4 TPC were not executed. Memory latency is mainly a function of where the requested piece of data is located in the memory hierarchy. Due to the SU(3) nature of the gauge fields they have only eight real degrees of freedom: the coefficients of the eight SU(3) generators. On the other hand, traditional search algorithms besides linear scan are latency bound since their iterations are data dependent. Sometimes there is conflict between small grain sizes (which give high parallelism) and high arithmetic intensity. (The raw bandwidth based on memory bus frequency and width is not a suitable choice since it can not be sustained in any application; at the same time, it is possible for some applications to achieve higher bandwidth than that measured by STREAM). Here's a question -- has an effective way to measure transistor degradation been developed? The last consideration is to avoid cache conflicts on caches with low associativity. By continuing you agree to the use of cookies. Memory bandwidth is basically the speed of the video RAM. Jim Jeffers, ... Avinash Sodani, in Intel Xeon Phi Processor High Performance Programming (Second Edition), 2016. Q: What is STREAM? N. Vijaykumar, ... O. Mutlu, in Advances in GPU Research and Practice, 2017. This is because part of the bandwidth equation is the clocking speed, which slows down as the computer ages. In such cases you’re better off performing back-to-back 32-bit reads or adding some padding to the data structure to allow aligned access. 2. The standard rule of thumb is to use buffers of size RTT×R for each link, where RTT is the average roundtrip time of a flow passing through the link. An alternative approach is to allow the size of each partition to be flexible. However, these guidelines can be hard to follow when writing portable code, since then you have no advance knowledge of the cache line sizes, the cache organization, or the total size of the caches. This is how most hardware companies arrive at the posted RAM size. To get the true memory bandwidth, a formula has to be employed. SPD is stored on your DRAM module and contains information on module size, speed, voltage, model number, manufacturer, XMP information and so on. This ideally means that a large number of on-chip compute operations should be performed for every off-chip memory access. CPU: 8x Zen 2 Cores at 3.5GHz (variable frequency) GPU: 10.28 TFLOPs, 36 CUs at 2.23GHz (variable frequency) GPU Architecture: Custom RDNA 2 Memory/Interface: 16GB GDDR6/256-bit Memory Bandwidth: 448GB/s If worse comes to worse, you can find replacement parts easily. Section 8.8 says more about the cache oblivious approach. These include the datapath switch [426], the PRELUDE switch from CNET [196], [226], and the SBMS switching element from Hitachi [249]. This leads to the following expression for this performance bound (denoted by MIS and measured in Mflops/sec): In Figure 3, we compare three performance bounds: the peak performance based on the clock frequency and the maximum number of floating-point operations per cycle, the performance predicted from the memory bandwidth limitation in Equation 1, and the performance based on operation issue limitation in Equation 2. Let us examine why. The memory bandwidth on the new Macs is impressive. Given the fact that on-chip compute performance is still rising with the number of transistors, but off-chip bandwidth is not rising as fast, in order to achieve scalability approaches to parallelism should be sought that give high arithmetic intensity. Table 1.1. Review by Will Judd , Senior Staff Writer, Digital Foundry Perhaps the simplest implementation of a switched backplane is based on a centralized memory shared between input and output ports. The basic idea is to consider the rows of the matrix as row vectors: Then, if one has the first two rows: a and b, both having been normalized to be of unit length, one can compute c = (a×b)*, that is, by taking the vector (cross) product of a and b and complex conjugating the elements of the result. In principle, this means that instead of nine complex numbers, we can store the gauge fields as eight real numbers. Fig. 25.5. First, a significant issue is the, Wilson Dslash Kernel From Lattice QCD Optimization, Bálint Joó, ... Karthikeyan Vaidyanathan, in, Our naive performance indicates that the problem is, Journal of Parallel and Distributed Computing. In effect, by using the vector types you are issuing a smaller number of larger transactions that the hardware can more efficiently process. [76] propose GPU throttling techniques to reduce memory contention in heterogeneous systems. Thus, one crucial difference is that access by a stride other than one, but within 128 bytes, now results in cached access instead of another memory fetch. Using fewer than 30 blocks is guaranteed to leave some of the 30 streaming multiprocessors (SMs) idle, and using more blocks that can actively fit the SMs will leave some blocks waiting until others finish and might create some load imbalance. These works do not consider data compression and are orthogonal to our proposed framework. While this is simple, the problem with this approach is that when a few output ports are oversubscribed, their queues can fill up and eventually start dropping packets. High-bandwidth memory (HBM) avoids the traditional CPU socket-memory channel design by pooling memory connected to a processor via an interposer layer. Cache friendly: Performance does not decrease dramatically when the MCDRAM capacity is exceeded and levels off only as MCDRAM-bandwidth limit is reached. When the packets are scheduled for transmission, they are read from shared memory and transmitted on the output ports. Such flexible-sized partitions require more sophisticated hardware to manage, however, they improve the packet loss rate [818]. By this time, bank 1 would have finished writing packet 1 and would be ready to write packet 14. Memory bandwidth is essential to accessing and using data. This trick is quite simple, and reduces the size of the gauge links to 6 complex numbers, or 12 real numbers. Computer manufactures are very conservative in slowing down clock rates so that CPUs last for a long time. Avoid unnecessary accesses far apart in memory and especially simultaneous access to multiple memory locations located a power of two apart. AMD 5900X and Ryzen 7 5800X: Memory bandwidth analysis AMD and Intel tested. DDR5 to the rescue! Signal integrity, power delivery, and layout complexity have limited the progress in memory bandwidth per core. Figure 2. - Identify the strongest components in your PC. Memory bandwidth and latency are key considerations in almost all applications, but especially so for GPU applications. Latency refers to the time the operation takes to complete. Having more than one vector also requires less memory bandwidth and boosts the performance: we can multiply four vectors in about 1.5 times the time needed to multiply one vector. What is more important is the memory bandwidth, or the amount of memory that can be used for files per second. In OWL [4, 76], intelligent scheduling is used to improve DRAM bank-level parallelism and bandwidth utilization, and Rhu et al. Third, as the line rate R increases, a larger amount of memory will be required. A simpler approach is to consider two-row storage of the SU(3) matrices. RAM): memory latency, or the amount of time to satisfy an individual memory request, and memory bandwidth, or the amount of data that can be 1. Organize data structures and memory accesses to reuse data locally when possible. The situation in Fermi and Kepler is much improved from this perspective. Lower memory multipliers tend to be more stable, particularly on older platform designs such as Z270, thus DDR4-3467 (13x 266.6 MHz) may be … You will want to know how much memory bandwidth your application is using. Effect of Memory Bandwidth on the Performance of Sparse Matrix-Vector Product on SGI Origin 2000 (250 MHz R10000 processor). Before closing the discussion on shared memory, let us examine a few techniques for increasing memory bandwidth. Latency refers to the time the operation takes to complete. Hyperthreading is useful to maximize utilization of the execution units and/or memory operations at a given time interval. The bytes not used will be fetched from memory and simply be discarded. In order to illustrate the effect of memory system performance, we consider a generalized sparse matrix-vector multiply that multiplies a matrix by N vectors. For example, in a 2D recurrence tiling (discussed in Chapter 7), the amount of work in a tile might grow as Θ(n2) while the communication grows as Θ(n). See Chapter 3 for much more about tuning applications for MCDRAM. If there are extra interfaces or chips, such as two RAM chips, this number is also added to the formula. A video card with higher memory bandwidth can draw faster and draw higher quality images. This request will be automatically combined or coalesced with requests from other threads in the same warp, provided the threads access adjacent memory locations and the start of the memory area is suitably aligned. We now have a … 25.3. Now this is obviously using a lot of memory bandwidth, but the bandwidth seems to be nowhere near the published limitations of the Core i7 or DDR3. When packets arrive at the input ports, they are written to this centralized shared memory. As the computer gets older, regardless of how many RAM chips are installed, the memory bandwidth will degrade. Based on the needs of an application, placing data structures in MCDRAM can improve the performance of the application quite substantially. ScienceDirect ® is a registered trademark of Elsevier B.V. ScienceDirect ® is a registered trademark of Elsevier B.V. URL: https://www.sciencedirect.com/science/article/pii/B9780124159334000090, URL: https://www.sciencedirect.com/science/article/pii/B978044482851450030X, URL: https://www.sciencedirect.com/science/article/pii/B978012416970800002X, URL: https://www.sciencedirect.com/science/article/pii/B9780124159938000025, URL: https://www.sciencedirect.com/science/article/pii/B9780123859631000010, URL: https://www.sciencedirect.com/science/article/pii/B9780128091944000144, URL: https://www.sciencedirect.com/science/article/pii/B9780128091944000259, URL: https://www.sciencedirect.com/science/article/pii/B978012803738600015X, URL: https://www.sciencedirect.com/science/article/pii/B9780128007372000193, URL: https://www.sciencedirect.com/science/article/pii/B9780128038192000239, Towards Realistic Performance Bounds for Implicit CFD Codes, Parallel Computational Fluid Dynamics 1999, To analyze this performance bound, we assume that all the data items are in primary cache (that is equivalent to assuming infinite, , we compare three performance bounds: the peak performance based on the clock frequency and the maximum number of floating-point operations per cycle, the performance predicted from the, CUDA Fortran for Scientists and Engineers, Intel Xeon Phi Processor High Performance Programming (Second Edition), A framework for accelerating bottlenecks in GPU execution with assist warps, us examine why. This code, along with operation counts, is shown in Figure 2. Table 1. By default every memory transaction is a 128-byte cache line fetch. You also introduce a certain amount of instruction-level parallelism through processing more than one element per thread. To satisfy QoS requirements, the packets might have to be read in a different order. 3. This can be achieved using different combinations of number of threads and outstanding requests per thread. It's always a good idea to perform a memory test on newly purchased RAM to test for errors. Figure 9.4. Thus, if thread 0 reads addresses 0, 1, 2, 3, 4, …, 31 and thread 1 reads addresses 32, 32, 34, …, 63, they will not be coalesced. Review by Will Judd , Senior Staff Writer, Digital Foundry Memory bandwidth values are taken from the STREAM benchmark web-site. Fig. If the workload executing at one thread per core is already maximizing the execution units needed by the workload or has saturated memory resources at a given time interval, hyperthreading will not provide added benefit. Now considering the formula in Eq. However, re-constructing all nine complex numbers this way involves the use of some trigonometric functions. In spite of these disadvantages, some of the early implementations of switches used shared memory. Let us first consider quadrant cluster mode and MCDRAM as cache memory mode (quadrant-cache for short). DDR4 has reached its maximum data rates and cannot continue to scale memory bandwidth with these ever-increasing core counts. In the System section, next to Installed memory (RAM), you can view the amount of RAM your system has. On the Start screen, click theDesktop app to go to the … (2,576) M … For the algorithm presented in Figure 2, the matrix is stored in compressed row storage format (similar to PETSc's AIJ format [4]). Heck, a lot of them are still in use in "embedded" designs and are still manufactured. If, for example, the MMU can only find 10 threads that read 10 4-byte words from the same block, 40 bytes will actually be used and 24 will be discarded. Jog et al. To make sure that all bytes transferred are useful, it is necessary that accesses are coalesced, i.e. To analyze this performance bound, we assume that all the data items are in primary cache (that is equivalent to assuming infinite memory bandwidth). The other three workloads are a bit different and cannot be drawn in this graph: MiniDFT is a strong-scaling workload with a distinct problem size; GTC’s problem size starts at 32 GB and the next valid problem size is 66 GB; MILC’s problem size is smaller than the rest of the workloads with most of the problem sizes fitting in MCDRAM. A higher clocking speed means the computer is able to access a higher amount of bandwidth. The theoretical peak memory bandwidth can be calculated from the memory clock and the memory bus width. Using the code at why-vectorizing-the-loop-does-not-have-performance-improvement I get a bandwidth … The size of memory transactions varies significantly between Fermi and the older versions. With more than six times the memory bandwidth of contemporary CPUs, GPUs are leading the trend toward throughput computing. 25.4 shows the performance of five of the eight workloads when executed with MPI-only and using 68 ranks (using one hardware thread per core (1 TPC)) as the problem size varies. Let's take a closer look at how Apple uses high-bandwidth memory in the M1 system-on-chip (SoC) to deliver this rocket boost. Memory bandwidth as a function of both access pattern and number of threads measured on an NVIDIA GTX285. These workloads are able to use MCDRAM effectively even at larger problem sizes. To estimate the memory bandwidth required by this code, we make some simplifying assumptions. High Bandwidth Memory (HBM) is a high-speed computer memory interface for 3D-stacked SDRAM from Samsung, AMD and SK Hynix. Another issue that affects the achievable performance of an algorithm is arithmetic intensity. Once enough bits equal to the width of the memory word are accumulated in the shift register, it is stored in memory. If the working set for a chunk of work does not fit in cache, it will not run efficiently. CPU speed, known also as clocking speed, is measured in hertz values, such as megahertz (MHz) or gigahertz (GHz). The more memory bandwidth you have, the better. In this case the arithmetic intensity grows by Θlparn)=Θlparn2)ΘΘlparn), which favors larger grain sizes. 25.7. In this figure, problem sizes for one workload cannot be compared with problem sizes for other workloads using only the workload parameters. Copyright © 2020 Elsevier B.V. or its licensors or contributors. While random access memory (RAM) chips may say they offer a specific amount of memory, such as 10 gigabytes (GB), this amount represents the maximum amount of memory the RAM chip can generate. Wikibuy Review: A Free Tool That Saves You Time and Money, 15 Creative Ways to Save Money That Actually Work. bench (74.8) Freq. In compute 1.x devices (G80, GT200), the coalesced memory transaction size would start off at 128 bytes per memory access. Memory bandwidth, on the other hand, depends on multiple factors, such as sequential or random access pattern, read/write ratio, word size, and concurrency [3]. This so-called cache oblivious approach avoids the need to know the size or organization of the cache to tune the algorithm. One vector (N = 1), matrix size, m = 90,708, nonzero entries, nz = 5,047,120. This formula involves multiplying the size of the RAM chip in bytes by the current processing speed. Trinity workloads in quadrant-cache mode with problem sizes selected to maximize performance. Fermi, unlike compute 1.x devices, fetches memory in transactions of either 32 or 128 bytes. The idea is that by the time packet 14 arrives, bank 1 would have completed writing packet 1. Fig. I tried prefetching but it didn't help. To do the comparison, we need to convert it to memory footprint. For our GTX 285 GPU the latency is 500 clock cycles, and the peak bandwidth is 128 bytes per clock cycle — the physical bus width is 512 bits, or a 64-byte memory block, and two of these blocks are transferred per clock cycle — so: assuming 4-byte reads as in the code in Section 1.4. For the sparse matrix-vector multiply, it is clear that the memory-bandwidth limit on performance is a good approximation. This idea was explored in depth for GPU architectures in the QUDA library, and we sketch only the bare bones of it here. This type of organization is sometimes referred to as interleaved memory. While SRAM has access times that can keep up with the line rates, it does not have large enough storage because of its low density. Three performance bounds for sparse matrix-vector product; the bounds based on memory bandwidth and instruction scheduling are much more closer to the observed performance than the theoretical peak of the processor. Although there are many options to launch 16,000 or more threads, only certain configurations can achieve memory bandwidth close to the maximum. For double-data-rate memory, the higher the number, the faster the memory and higher bandwidth. A more comprehensive explanation of memory architecture, coalescing, and optimization techniques can be found in Nvidia's CUDA Programming Guide [7]. While a detailed performance modeling of this operation can be complex, particularly when data reference patterns are included [14–16], a simplified analysis can still yield upper bounds on the achievable performance of this operation. Since the number of floating-point instructions is less than the number of memory references, the code is bound to take at least as many cycles as the number of loads and stores. AMD Ryzen 9 3900XT and Ryzen 7 3800XT: Memory bandwidth analysis AMD and Intel tested. This is the ratio of computation to communication. However, be aware that the vector types (int2, int4, etc.) Notice that MiniFE and MiniGhost exhibit the cache unfriendly or sweet spot behavior, and the other three workloads exhibit the cache friendly or saturation behavior. introduce an implicit alignment of 8 and 16 bytes, respectively. With an increasing link data rate, the memory bandwidth of a shared memory switch, as shown in the previous section, needs to proportionally increase. Another reason is that new programs often need more power, and this continuous need for extra power begins to burn out the CPU, reducing its overall processing abilities. The incoming bits of the packet are accumulated in an input shift register. Tim Kaldewey, Andrea Di Blas, in GPU Computing Gems Jade Edition, 2012. Right click the Start Menu and select System. Second, use the 64-/128-bit reads via the float2/int2 or float4/int4 vector types and your occupancy can be much less but still allow near 100% of peak memory bandwidth. Kingston Technology HyperX FURY 2666MHz DDR4 Non-ECC CL15 DIMM 16 DDR4 2400 MT/s (PC4-19200) HX426C15FBK2/16 Second, the access times of memory available are much higher than required. For a switch with N=32 ports, a cell size of C=40 bytes, and a data rate of R=40 Gbps, the access time required will be 0.125 nanosec. Processor speed refers to the central processing unit (CPU) and the power it has. It's measured in gigabytes per second (GB/s). On the other hand, DRAM is too slow, with access times on the order of 50 nanosec (which has increased very little in recent years). The STREAM benchmark memory bandwidth [11] is 358 MB/s; this value of memory bandwidth is used to calculate the ideal Mflops/s; the achieved values of memory bandwidth and Mflops/s are measured using hardware counters on this machine. Many prior works focus on optimizing for memory bandwidth and memory latency in GPUs. One of the key areas to consider is in the number of memory transactions in flight. As indicated in Chapter 7 and Chapter 17, the routers need buffers to hold packets during times of congestion to reduce packet loss. As we saw when optimizing the sample sort example, a value of four elements per thread often provides the optimal balance between additional register usage, providing increased memory throughput and opportunity for the processor to exploit instruction-level parallelism. Since all of the Trinity workloads are memory bandwidth sensitive, performance will be better if most of the data is coming from the MCDRAM cache instead of DDR memory. Finally, we store the N output vector elements. Computers need memory to store and use data, such as in graphical processing or loading simple documents. ​High bandwidth memory (HBM); stacks RAM vertically to shorten the information commute while increasing power efficiency and decreasing form factor. The problem with this approach is that if the packets are segmented into cells, the cells of a packet will be distributed randomly on the banks making reassembly complicated. The sparse matrix-vector product is an important part of many iterative solvers used in scientific computing. 25.5 summarizes the best performance so far for all eight of the Trinity workloads. 25.6 plots the thread scaling of 7 of the 8 Trinity workloads (i.e., without MiniDFT). All experiments have one outstanding read per thread, and access a total of 32 GB in units of 32-bit words. In practice, the largest grain size that still fits in cache will likely give the best performance with the least overhead. A shared memory switch where the memory is partitioned into multiple queues. Commercially, some of the routers such as the Juniper M40 [742] use shared memory switches. Memory is one of the most important components of your PC, but what is RAM exactly? For Trinity workloads, MiniGhost, MiniFE, MILC, GTC, SNAP, AMG, and UMT, performance improves with two threads per core on optimal problem sizes. We use cookies to help provide and enhance our service and tailor content and ads. In this case, for a line rate of 40 Gbps, we would need 13 (⌈50undefinednanosec/8undefinednanosec×2⌉) DRAM banks with each bank having to be 40 bytes wide. We make the simplifying assumption that Bw = Br, and then can divide out the bandwidth, to get the arithmetic intensity. The data must support this, so for example, you cannot cast a pointer to int from array element int[5] to int2∗ and expect it to work correctly. Max Bandwidth の部分には、この メモリの種類 が書かれています。 スペック不足などでメモリを増設する時に確認したいのは主にこの部分です。 PC3-10700と書かれていますが、PC3の部分でメモリの規格(メモリの形状)を表しています。 Windows 8 1. That old 8-bit, 6502 CPU that powers even the "youngest" Apple //e Platinum is still 20 years old. Returning to Little's Law, we notice that it assumes that the full bandwidth be utilized, meaning, that all 64 bytes transferred with each memory block are useful bytes actually requested by an application, and not bytes that are transferred just because they belong to the same memory block. Therefore, I should be able to measure the memory bandwidth from the dot product. Cache and Memory Latency Across the Memory Hierarchy for the Processors in Our Test System. Little's Law, a general principle for queuing systems, can be used o derive how many concurrent memory operations are required to fully utilize memory bandwidth. There are two important numbers to pay attention to with memory systems (i.e. Also, those older computers don't run as "hot" as newer ones because they are doing far less in terms of processing than modern computers that operate at clock speeds that were inconceivable just a couple of decades ago. On the other hand, the impact of concurrency and data access pattern require additional consideration when porting memory-bound applications to the GPU. A 64-byte fetch is not supported. But keep a couple of things in mind. The same table also shows the memory bandwidth requirement for the block storage format (BAIJ) [4] for this matrix with a block size of four; in this format, the ja array is smaller by a factor of the block size. Figure 16.4. We observe that the blocking helps significantly by cutting down on the memory bandwidth requirement. This could lead to something called the “hot bank” syndrome where the packet accesses are directed to a few DRAM banks leading to memory contention and packet loss. 25.3). Referring to the sparse matrix-vector algorithm in Figure 2, we get the following composition of the workload for each iteration of the inner loop: 2 * N floating-point operations (N fmadd instructions). Finally, the time required to determine where to enqueue the incoming packets and issue the appropriate control signals for that purpose should be sufficiently small to keep up with the flow of incoming packets. Michael McCool, ... James Reinders, in Structured Parallel Programming, 2012. Most contemporary processors can issue only one load or store in one cycle. We show some results in the table shown in Figure 9.4. However, the problem with this approach is that it is not clear in what order the packets have to be read. This is an order of magnitude smaller than the fast memory SRAM, the access time of which is 5 to 10 nanosec. One reason is that the CPU often ends up with tiny particles of dust that interfere with processing. Increasing the number of threads, the bandwidth takes a small hit before reaching its peak (Figure 1.1a). First, fully load the processor with warps and achieve near 100% occupancy. When a stream of packets arrives, the first packet is sent to bank 1, the second packet to bank 2, and so on. Considering 4-byte reads as in our experiments, fewer than 16 threads per block cannot fully use memory coalescing as described below. If the search for optimal parameters is done automatically it is known as autotuning, which may also involve searching over algorithm variants as well.

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